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A Survey of User Perspectives on Security and Privacy in a Home
Networking Environment
NANDITA PATTNAIK, SHUJUN LI, and JASON R.C. NURSE, University of Kent, UK
The security and privacy of smart home systems, particularly from a home user’s perspective, have been a very active research
area in recent years. However, via a meta-review of 52 review papers covering related topics (published between 2000 and
2021), this paper shows a lack of a more recent literature review on user perspectives of smart home security and privacy since
the 2010s. This identied gap motivated us to conduct a systematic literature review (SLR) covering 126 relevant research
papers published from 2010 to 2021. Our SLR led to the discovery of a number of important areas where further research is
needed; these include holistic methods that consider a more diverse and heterogeneous range of home devices, interactions
between multiple home users, complicated data ow between multiple home devices and home users, some less-studied
demographic factors, and advanced conceptual frameworks. Based on these ndings, we recommended key future research
directions, e.g., research for a better understanding of security and privacy aspects in dierent multi-device and multi-user
contexts, and a more comprehensive ontology on the security and privacy of the smart home covering varying types of home
devices and behaviors of dierent types of home users.
CCS Concepts: • Security and privacy →Human and societal aspects of security and privacy.
Additional Key Words and Phrases: Security, privacy, systematic literature review, user perspectives, home, networking, IoT,
smart devices
1 INTRODUCTION
Modern information and telecommunication technologies (ICT) have made today’s homes more connected and
digitized. With the rapid advancement of articial intelligence (AI) technologies and their use in modern homes,
the more traditional term “home network” is increasingly replaced by more recent one “smart home", which
often covers the use of IoT (Internet of Things) home devices with “smart” functionalities and relying on data
exchanges with the Internet. Today’s smart homes are equipped with many computing/networking and smart
devices, sensors, systems, and software applications. According to a 2020 report [
101
], the average number of
connected devices in a household in most Western countries is over 7. All home devices communicate with each
other and the network/Internet following a range of dierent protocols, while interacting with internal and
external entities including home users and other individuals [
19
]. This continuously evolving home networking
environment oers a multitude of benets and opportunities to home users, but also simultaneously presents
many challenges, including varied security threats and privacy issues.
Current research in this area has a signicant focus on understanding the home users’ perspectives, reecting on
their awareness [
58
,
95
,
188
,
202
], behavior [
8
,
57
,
77
,
106
,
179
], actions [
18
,
31
,
53
,
138
] and concerns [
13
,
104
,
203
].
In order to understand the scope and nature of these studies and ascertain the direction of future research, it is
important to collate, review and analyze the relevant research in this eld. Past reviews on related topics are more
Authors’ address: Nandita Pattnaik, np407@kent.ac.uk; Shujun Li, S.J.Li@kent.ac.uk; Jason R.C. Nurse, J.R.C.Nurse@kent.ac.uk, Institute of
Cyber Security for Society (iCSS) & School of Computing, University of Kent, Canterbury, UK, CT2 7NP.
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0360-0300/2022/8-ART $15.00
https://doi.org/10.1145/3558095
ACM Comput. Surv.
2 • Nandita Panaik, Shujun Li, and Jason R.C. Nurse
focused on product-centric analyses [
79
,
160
,
161
,
188
], are technical and security related [
17
,
78
,
151
], consider
the smart home in general [
54
,
184
,
194
] or are purely privacy oriented [
96
]. Reviews that covered related user
perspectives studies, are either too dated [80, 191], or have a narrower scope, e.g., privacy only [96], or focused
on a particular segment of home users (older users’ privacy attitude) [142, 180].
Hence, our goal in this paper is to review the current research in this eld and determine the areas where further
research is necessary. With this in view, we need to explain two important terms, which are used throughout this
paper. Firstly, we consider the term “home” as a relatively broader concept, covering traditional family residences,
shared student accommodations, shared ats/houses, residential care homes, and nursing homes. We use the
term “home network” to signify a network of all computing and connected devices in a home that may or may
not be considered smart devices. When we use the term “smart home” or “smart home network”, we refer to a
slightly narrower concept, i.e., a home network that includes at least one or more smart devices, which can be
controlled from a smart device or a personal computer.
1
Note that smart mobile devices and wearables may not
be considered as smart devices by some home users and vendors, so the term “smart device” and “smart home”
can have dierent meanings for dierent people. Secondly, “user perspectives” in our paper will incorporate a
broad range of topics including mainly the following:
UP1:
home users’ behaviors, awareness, perceptions, attitudes, practices and concerns relating to security and
privacy of the home network;
UP2: the relevant contexts in which UP1 occur or change;
UP3: eects of dierent demographic factors such as age, gender on UP1; and
UP4: theoretical frameworks that can help explain UP1.
According to the systematic meta-review we conducted as part of the research reported in this paper, covering
52 literature reviews published between 2000 – 2021 (Table 2), only 10 papers cover some (mostly an incomplete
set of) topics related to ‘user perspectives’ and only one paper [
80
] published in 2012, covers a more complete
discussion of related topics. The results of our meta-review indicate a gap of more recent literature review on
security and privacy of smart home systems, from a home user’s perspectives.
Our work was conducted in a two-staged approach. In the rst stage, we conducted the above-mentioned
systematic meta-review, to have a better understanding of related literature review papers. The results of the
meta-review helped shape the methodology of a subsequent systematic literature review (SLR) in the second
stage.
Key contributions of our work and noteworthy ndings from our SLR are summarized below.
(1)
Methodologically, we used a meta-review to systematically examine past literature reviews and to facilitate
design of a follow-up SLR, which is a review method that has not been used in similar past work.
(2)
Compared with past literature review papers, our SLR has the most comprehensive coverage of dierent
user perspectives related to security and privacy of smart home systems, and covers more recent research
papers from 2010 until 2021.
(3)
A number of key ndings and recommendations for future research directions are obtained from our SLR,
many of which have not been discussed in previous review papers. We list these below.
•
The hybrid nature of a modern home with multiple (inter-)connected devices, including dierent types of
traditional and “smarter” devices, is still under-studied.
•The existence of multiple and dierent types of home users has not been suciently considered.
•
Study of home network and related security and privacy issues in dierent context and various cross-
contextual eects are still under-researched areas.
•
More research is needed to better understand data ows across multiple devices and users and in dierent
contexts.
1A similar denition can be found at https://www.lexico.com/denition/smart_home.
ACM Comput. Surv.
A Survey of User Perspectives on Security and Privacy in a Home Networking Environment • 3
•Some demographic factors such as location and income of home users are less studied than others.
•
Research on home users’ perspectives in relation to the security and privacy of the home network is
predominately focused on a small number of smart devices, e.g., smart speakers and smart cameras.
•
More advanced theoretical and conceptual frameworks, such as smart home security ontologies, need
to be developed to support a more holistic view of security and privacy aspects of a home network
environment.
The rest of this paper is structured as follows. Section 2 discusses the general methodology followed to conduct
both studies. Sections 3 and 4 explain the methodology and results of the two stages (the meta-review and the
SLR), respectively. Note that our meta-review plays the role of the related work section of a more traditional
literature review paper. Section 5 summarizes our main ndings and recommendations for future research, while
the later section concludes the paper.
2 GENERAL METHODOLOGY
As mentioned in the previous section, the methodology we followed is two-staged: 1) a meta-review to gather the
related reviews in this eld more systematically and to help formulate research questions for the SLR, and 2) an
SLR to collate and analyze the original research in this eld. For both the meta-review and the SLR, we followed
the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method [
128
], a widely used
procedure for conducting SLRs. The procedures followed in both stages of our study are illustrated in Fig. 1.
Meta-Review SLR
ACM Digital Library
(�=315)
Scopus
(�=1,647)
IEEE Xplore
(�=708)
Merging all search results
(�=2,670)
Removing duplicates
(�=1,647)
Screening based on titles and abstracts
(�=257)
Final selection based on fulltext
(�=52)
ACM Digital Library
(�=61)
Scopus
(�=2,521)
IEEE Xplore
(�=424)
Merging all search results
(�=3,006)
Removing duplicates
(�=2,521)
Screening based on titles and abstracts
(�=226)
Selection based on fulltext
(�=102)
+Snowballing (�=24)
Final selection (�=126)
Fig. 1. The PRISMA procedures used for the meta-review and the SLR
ACM Comput. Surv.
4 • Nandita Panaik, Shujun Li, and Jason R.C. Nurse
The PRISMA procedure to identify eligible papers for the systematic review process includes four main steps:
(1) identifying pertinent records, (2) screening the selected records based on the exclusion and inclusion criteria,
(3) assessing the eligible records, and (4) selecting eligible items for the nal study. Since the results of the
meta-review were produced systematically and contributed to the selection of our research questions for the
SLR, we decided to present both the above scientic process in one ow diagram presented in Fig. 1. For ease
of understanding, both these processes are color coded (blue for the SLR, and green for the meta-review) to
represent two dierent processes.
For both stages, a selection of three major scientic databases, Scopus, ACM Digital Library and IEEE Xplore,
were considered. We decided to include Scopus as it is regarded as a very comprehensive and interdisciplinary
database [
21
]. Note that Scopus is a product of the largest scientic publisher Elsevier, so research papers
published by Elsevier are well covered by Scopus. In addition, we observed that research papers published by
other mainstream publishers such as Springer and John Wiley & Sons, Inc. are substantially indexed by Scopus.
We decided to include ACM Digital Library and IEEE Xplore as additional databases because ACM and IEEE are
the two most important subject-specic publishers for cyber security and smart home research. Although the
same query was used for all the databases, each database oered dierent searching tools, i.e., Scopus supported
searching directly into paper title, abstract and keywords (called “meta data”), but ACM Digital Library and IEEE
Xplore did not oer such a direct search option, so we decided to search into the abstract only. The results were
ltered to include only ‘Journal articles’, ‘Conference proceedings’, ‘Conference reviews’, ‘Reviews’, and ‘Articles
in press’, published in the English language. Specic keywords used, the inclusion and exclusion criteria for each
of the review and the period of search, have been included in the respective methodology sections.
To store, categorize and analyze our data in both stages, we used a widely used research software system called
MAXQDA (http://www.MAXQDA.com/). MAXQDA has several useful tools to support dierent qualitative and
quantitative analysis tasks such as “Smart coding tool”, “Document variable analysis”, “Visual tools”, “Memos”,
“MaxDictio” which were very helpful in our analysis.
3 META-REVIEW
The meta-review aims to identify, collect, analyze and synthesize the review papers on the subject of security
and privacy at home, and helps to formulate the research questions for the main SLR. The meta-review concept
is similar to scope review [132] that can be used as a precursor to an SLR to help guide the design of the SLR.
3.1 Methodology
Following our research aim, the meta-review focused on the research question: How have existing review papers
in the area of security and privacy of home networking covered research on user perspectives?
3.1.1 Search Keywords. Table 1 shows the keywords we used to conduct our search queries. The search strategy
includes three main components: the rst subset of keywords capture the home networking and smart home
context, the second limit our searches to security and privacy-related papers, and the last one covers several
typical keywords indicating the nature of the paper as a review or SoK (systematization of knowledge) paper.
Table 1. The list of keywords used for the meta-review
(Home AND (Network OR Networking OR Smart OR Computer OR Computing OR Internet OR Device))
AND (Security OR Privacy)
AND (Review OR Survey OR Overview OR Systematisation OR Systematization OR Systematic OR SoK)
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A Survey of User Perspectives on Security and Privacy in a Home Networking Environment • 5
To follow the screening and eligibility checking steps of the PRISMA process, a set of exclusion and inclusion
criteria were established. A paper meeting any one of the exclusions (or inclusion) criteria was excluded (or
included).
3.1.2 Exclusion Criteria. Papers meeting the following exclusion criteria were excluded:
•Papers published before 2001
•Non-English papers
•Papers that do not cover the home context (e.g., those covering industrial IoT)
•Conference reviews or book chapters
•Papers that do not cover any security or privacy factors
•
Papers which are not review papers excepting non-review papers that report any taxonomy covering
security, privacy or user perspectives of the home network
3.1.3 Inclusion Criteria. After excluding papers based on the exclusion criteria, those meeting at least one of the
following inclusion criteria were selected:
•Papers focusing on issues and solutions in the area of security and privacy of the home network
•Papers that systematically reviewed smart home and home network related products or applications
The non-review papers reporting on ontology or taxonomy relevant to the subjects were considered as
pseudo-reviews and were included in the study because they normally systematically conceptualize relevant
topics.
In the following, we present the results of the meta-review.
3.2 Results & Discussion
Figure 1 (colored in green) shows the results from each step of the meta-review. The initial searches gave us
2,670 papers in total. After removing duplicates, we had 1,647 papers to screen. Following the exclusion criteria
in Section 3.1.2, we ended up with 257 papers for further screening. After a scan of those papers’ abstracts,
introduction and conclusion sections and applying our inclusion criteria presented in Section 3.1.3, 52 papers
were included for nal analysis. The screening and ltering steps gave us a clear indication of reviews that were
conducted in the past on relevant topics, helping to inform the second stage of our work (i.e., the SLR). Table 2
presents an overall comparison of related study with the current research. Note that although all included papers
are review papers, only some can be considered SLR (i.e., others were not done following a systematic approach).
Table 3 depicts the thematic categorization of the 52 papers in our study based on the broad overall theme that
the reviews conveyed.
3.2.1 Security & Privacy in General. Nineteen papers covered by the meta-review focus on various common
issues arising from security and privacy problems in a smart home. DeFranco and Kassab’s paper [
52
] covered the
broad theme of general research avenues around smart home. While Kuyucu et al
.
[
99
] reviewed papers relating
to both privacy and security related issues and solutions, Liao et al
.
[
109
] explored security problems, challenges,
techniques used, and solutions available from a mobile computing point of view. Talal et al
.
[
175
] focused on
security issues of tele-medicine environment exploring smart home issues and solutions in general and hardware
sensors, protocols, wireless network, security architecture, in particular. Philip et al
.
[
147
] published a similar
study on home health monitoring systems. Bolton et al
.
[
33
] surveyed the security and privacy challenges of
virtual assistants such as ‘Siri’ by Apple. Barriga A. and Yoo [
23
] discussed existing security mechanisms and
approaches in a smart home automation system, while Batalla et al
.
[
25
] analyzed the security requirements
and countermeasures used in a general IoT architecture and suggested an extendable home area network with
multi-level privacy and security to be managed by a trusted external actor to lessen the burden on home users.
ACM Comput. Surv.
6 • Nandita Panaik, Shujun Li, and Jason R.C. Nurse
Table 2. Comparison of the current study with related work
Holistic
Multi-Device
Awareness
Concerns
Behavior
Multi-User
Demographics
Contextual
Theoretical
[111] ✓ ✓
[35, 90] ✓ ✓
[180] ✓ ✓ ✓ ✓
[80] ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
[191] ✓ ✓ ✓
[198] ✓ ✓ ✓ ✓ ✓
[2, 25, 115] ✓ ✓
[10, 67] ✓✓✓
[14] ✓ ✓ ✓ ✓ ✓
[142] ✓ ✓ ✓ ✓
[24] ✓ ✓ ✓ ✓ ✓ ✓
[78] ✓✓✓
[17, 62, 66] ✓ ✓
[158, 184] ✓ ✓ ✓
[96] ✓ ✓ ✓ ✓ ✓
[12, 99] ✓ ✓ ✓ ✓
[16, 124] ✓ ✓ ✓
[41, 74, 75, 133, 185] ✓ ✓
[51, 55] ✓ ✓
[45, 109, 151, 159, 175] ✓ ✓ ✓
[131, 194] ✓ ✓ ✓
[152] ✓ ✓ ✓ ✓
[54] ✓ ✓ ✓ ✓
[33, 52, 69, 172] ✓ ✓ ✓ ✓ ✓
[43] ✓ ✓ ✓
[11, 127] ✓ ✓
[147] ✓✓✓
[107] ✓ ✓ ✓ ✓ ✓
2022 Our work ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Year(s)
Reference(s)
Subject Focus
SLR
Meta-Review
Security
Privacy
Smart Home
User Perspectives
2000–2016
2017
2018
2019
2020
2021
Other topics covered in some reviews include smart home data protection issues [
43
] and security issues in
dierent layers of smart home network [172].
Three papers chose to focus on specic areas in the smart home security. These include, Ambient Intelligence
(AmI) applications and their privacy issues by Lopez et al
.
[
111
], major wireless protocols such as Zigbee and
Z-wave by Marksteiner et al
.
[
115
], and, and security and protection mechanism in face detection techniques
by Fatima et al
.
[
55
]. Papers also looked into general smart home related topics, i.e., Vasanth et al
.
[
185
] who
discussed on smart home automation and trust building factors, Whereas, Michler et al
.
[
124
] investigated
ACM Comput. Surv.
A Survey of User Perspectives on Security and Privacy in a Home Networking Environment • 7
Table 3. Categorization of review papers into dierent categories (period: 2000–2021)
Category
2000–2016
2017
2018
2019
2020
2021
(#)
Security &
privacy in
general
[111] [25, 115] [17, 23] [12, 99, 124,
185]
[55, 109,
147, 151,
159, 175]
[33, 43, 52,
172] 19
User perspectives
[80, 180,
191, 198] [14, 24, 142] [96] [124] – [107] 10
Threats and
attacks [90] [2, 25] [78] [16, 74] [45, 51, 109] – 9
Security &
privacy solutions – – [17, 62, 66,
158]
[41, 75, 99,
133]
[131, 151,
159] [11, 127] 13
Smart devices [35, 90] [10, 67] [184] – [54, 152,
194] [69] 9
research work on trust-building factors in consumer IoT products in four areas including smart home. Reviews
also considered alternative approaches to dierent security issues in a home network such as utilizing fog-
computing architecture [
151
], smart home safety and security using Ardunio platform [
159
] and using software
dened networking (SDN) [17] to control home network security.
3.2.2 User Perspectives. We identied ten papers that reviewed the literature on privacy and security issues from
home users’ perspectives. Out of all 10 papers, Howe et al
.
[
80
]’s research was the only paper which touched
upon many of the areas we wanted to review, although it is a very old review (2012). This research focused on
the psychology and factors inuencing users’ security behaviors and decisions. It explored various demographic
characteristics, information sources for home users, users’ understanding of security risks, their perception of
security behaviors and defensive security actions.
Wilson et al
.
[
191
] analyzed 150 relevant papers and organized their ndings into three broad themes of
1) growth of smart home from ‘functional’, ‘instrumental’ and ‘socio-technical’ viewpoints, 2) users and their
use of smart home with the subcategories of ‘prospective users’, ‘interactions and decisions’ and ‘technology
in the home’, and 3) challenges in a smart home covering hardware and software, design and domestication
issues. Alotaibi et al
.
[
14
] reviewed research work on security awareness and education amongst home users
recommended an individualized approach to provide information to users based on their existing awareness level.
Michler et al
.
[
124
] analyzed user perception from the angle of trust to understand smart home adoption issues.
Two papers explored the elderly users’ prospective on using smart home. Pal et al
.
[
142
]’s systematic review
on the elderly users’ perspective on smart homes, observed that the elderly population have serious security
and privacy concerns on the use of smart devices. In another systematic review, Yusif et al
.
[
198
] focused on
assistive technology use by older people and noted that 34% of the articles examined, recognized privacy as a
major concern for the older adults.
The other four papers diered widely in their topic choices. After having systematically reviewed privacy-
related papers, Kraemer and Flechais [
96
] concluded that contextual privacy at home and privacy behaviors
were very under-researched areas. The concept of privacy paradox and related studies were covered in two
reviews [
24
,
180
]. Li et al
.
[
107
] conducted a systematic review to nd the motivation, barriers and risk of smart
home adoption from a consumer’s point of view.
3.2.3 Threats and Aacks. We found nine papers reviewing various aspects of security threats, attack types.
Abrishamchi et al
.
[
2
] discussed dierent types of side-channel threats in a smart home, giving a categorical
ACM Comput. Surv.
8 • Nandita Panaik, Shujun Li, and Jason R.C. Nurse
view of dierent devices and systems layers in a smart home that leaks private data. Hearteld et al
.
[
78
] on the
other hand, produced a taxonomic view of the possible cyber-physical threats and their impact on smart home.
Alrawi et al
.
[
16
] produced a systematized view of the research literature on smart home security arena, under
4 major categories: ‘device’, ‘mobile application’, ‘cloud endpoint’ and ‘communication’. Papers were further
sub-categorized into ‘Attack vector’, ‘Mitigation’, and ‘Stakeholder’ and evaluated with 45 IoT devices to identify
the research gaps. Researchers also explored the security vulnerabilities by dierent IoT devices [
51
] and smart
home devices [
45
]. Batalla et al
.
[
25
] classied the security threats in home area network devices by referring to
the 3-layered approach of ENISA (European Union agency for Cybersecurity), i.e., perceptual, network and the
application layer attack and physical attacks. Implantable, wearables and embedded sensors were another topic
of discussion [90] along with security measures in mobile computing [109].
3.2.4 Security & Privacy Solutions. Thirteen papers focused on solutions to specic security and privacy problems
in a smart home. Chakraborti et al
.
[
41
] reviewed research work on software solutions and embedded solutions
in a smart home. Two of the papers [
131
,
158
] reviewed blockchain-based solutions for addressing security and
privacy problems of a smart home. Three papers focused on dierent areas of smart home solutions, including
current fog-based literature [
151
], research on software-dened networks (SDN) [
17
] and smart home safety
and security systems focusing specically based on the Arduino platform [
159
]. Kuyucu et al
.
[
99
] reviewed
papers both on issues and solutions. We found six papers reviewing the existing literature on authentication
schemes and various security threats to them. Four papers [
11
,
62
,
127
,
133
] provided a general classication of
dierent authentication schemes, threats and attacks on IoT devices in a smart home. Other such as Ghazali and
Zakaria [
66
] extensively reviewed the biometric factors reecting on the authentication mechanisms inside a
smart home whereas, Gupta et al
.
[
75
] focused on a comparative study of dierent encryption algorithms used in
IoT platform and proposed solutions to increase energy eciency of various systems.
3.2.5 Smart Home Devices. Nine papers covered in the meta-review focused on smart home devices, with-
out looking at the system or user level aspects where multiple devices form a home network. Varghese and
Hayajneh [
184
] reviewed research on security and privacy of dierent categories of smart home devices. Ray
et al
.
[
152
] provided an in-depth discussion on IoT-based biosensors. Four other papers covered other specic
areas including smart TV [
10
], hybrid broadcasting broadband TV (HbbTV) techniques [
67
], video surveillance
methods [
35
], implantable and wearable medical devices, detectors and control systems such as temperature
sensors or smoke detectors [
90
]. Edu et al
.
[
54
] reviewed research work on smart home personal assistants
(SPA) to examine the main security and privacy issues, features that characterize known attacks, limitations of
countermeasures. Features and challenges of smart gateway systems [
194
] and assisted smart home technologies
for elderly people [69] are two other areas of discussion under this category.
3.2.6 Discussions & Research estions Identified for the SLR. The meta-review led to several key ndings.
•
First, although ten papers covered user perspectives in dierent ways, the discussions have not considered
papers from both the security and privacy angle. The reviews have reected on the privacy issues [
96
,
180
], educational awareness [
14
] and general challenges users face with very little focus on security and
privacy [
191
]. The “User Perspective” as discussed in Section 1, comprises a much broader domain. Only
Howe et al
.
[
80
] discussed a more comprehensive details of user perspectives including behaviors and
practices of users, but the study is relatively old (published in 2012). There is therefore a need of collating
and synthesizing more studies in this growing area.
•
Second, 42 review papers collectively cover dierent aspects of smart home research, including specic
(types of) smart home devices, privacy and security issues, and technical solutions. We noticed the absence
of wider discussions and a more holistic view of a smart home where multiple and heterogeneous devices
are interacting with each other and home users.
ACM Comput. Surv.
A Survey of User Perspectives on Security and Privacy in a Home Networking Environment • 9
•
Third, as reported in [
96
], contextual aspects of security and privacy of home network have been much
less studied.
•
Fourth, among the ten review papers covering user perspectives, demographic factors are mentioned in
only one early review conducted by Howe et al. (2012) [80].
•
Fifth, although not a systematic review, Howe et al
.
[
80
] conducted a very comprehensive review of papers
covering user perspectives, published before 2010 and was a main motivation of our study. Hence, this
work focuses on papers published since 2010.
Based on the above ndings, we decided to dene the following more focused research questions for the SLR
in the second stage of our work, as presented in Table 4.
Table 4. Research questions (RQs) identified for the follow-up SLR via the meta-review
RQ1
Has the current literature paid attention to the security and privacy perspectives of users within a home
network in a holistic manner?
RQ2
Is there any research exploring home users perspectives on security and privacy of multiple inter-
connected devices in a home network?
RQ3 What is the current research on users’ awareness of security and privacy issues of a home network?
RQ4
To what extent researchers have explored the security and privacy concerns of users in a home network?
RQ5 What type of user security behaviors and practices in a home network have been researched?
RQ6
What research has been conducted to understand the dierence in users security and privacy behaviors
and practices in a multi-user home network?
RQ7
What demographic factors have been studied while exploring home users’ perspectives on security and
privacy aspects in a home network?
RQ8
How have dierent contexts of a home network been considered when studying home users’ perspectives
regarding security and privacy aspects?
RQ9
What theoretical and conceptual frameworks have been proposed and used to facilitate studies on user
perspectives regarding security and privacy aspects of a home network?
As is evident from the paper, the boundary between RQ3, RQ4 and RQ5 is not a clear-cut one. Broadly speaking,
RQ3 focuses more on the knowledge of home users on facts related to security and privacy matters in the home
networking context; RQ4 focuses more on home users’ concerns (i.e., perceived risks and problems), including
not only concerns caused by awareness of genuine security and privacy issues, but also those caused by false
perception or misunderstanding of (possibly non-existing) security and privacy issues; and RQ5 looks more
at what home users actually do (behaviors and practices) and the reasons behind them (attitudes, motivation,
perception). RQ5 has a broader scope and may be argued to include RQ3 and RQ4 as two sub-questions, since
actual behaviors and practices may also be caused by (lack of) awareness and/or concerns.
4 SYSTEMATIC LITERATURE REVIEW (SLR)
The SLR was conducted in line with the 9 research questions identied via the meta-review reported at the end
of the previous section, examining related original (i.e., non-review) research work in the literature from 2010 till
2021. There are two reasons why we decided to exclude papers published before 2010. First, we wanted to focus
on more recent research (the past decade), which is a common practice for SLRs [
89
,
182
]. Our results showed
that relevant research was indeed more done in the past several years (see Figure 2). Second, Howe et al
.
’s review
work [80] conducted in 2012 covered original research papers published before 2010 quite comprehensively.
ACM Comput. Surv.
10 • Nandita Panaik, Shujun Li, and Jason R.C. Nurse
4.1 Methodology
As mentioned in Section 2, we followed the same PRISMA method [
128
,
140
] for the SLR, following the exclusion
and inclusion criteria explained below. Similar to what we did for the mata-review, we also considered the term
“home” in a broader sense.
4.1.1 Exclusion Criteria. Papers meeting the following exclusion criteria were excluded:
•Papers published before 2010
•Non-English papers
•
Papers that are not related to a home network context or do not include a signicant coverage of home
networks
•Papers that do not cover any security or privacy aspects
•Papers that do not cover home user issues
•
Papers that have been considered in the meta-review reported in the previous section, or papers that do
not report original research work.
4.1.2 Inclusion Criteria. Any topics which meets at least one of the relevant areas in the RQs listed in Table 4.
4.1.3 Information Sources & Search Strategy. As mentioned for the general methodology in Section 2, the same
three databases, i.e., Scopus, ACM Digital Library and IEEE Xplorer, were used to search for related research
papers. The scope of our SLR was decided mainly by the result of the meta-review which are reected in our
research questions Table 4. The research questions which were formulated as the result of the meta-review guided
the process of a keyword selection. The search queries we used for the SLR are formed by four required sets of
keywords each covering a dierent aspect of our RQs: the context of home network or smart home, security or
privacy, users, and behaviors (see Table 5).
Table 5. The keywords used in the search queries of the SLR
(Home AND (Network* OR Smart OR Comput* OR Internet OR Device))
AND (Security OR Privacy)
AND (User OR Human OR People OR Customer OR Person)
AND
(Perception OR Awareness OR Concern OR Behaviour OR Behavior OR Worry OR Action OR Decision)
4.2 Results
4.2.1 Papers Selected. The papers returned from the database searches totaled: 2,521 from Scopus, 61 from ACM
Digital Library, and 424 from IEEE Xplore. After removing duplicates, those papers were rst screened based on
their titles and abstracts, by applying the exclusion criteria. This resulted in 226 papers eligible for more detailed
screening and selection based on their full text. This led to the exclusion of 124 papers and 102 eligible papers
were selected.
We performed an additional snowballing-based process [
65
] to identify more relevant papers by analyzing
references of the 102 papers selected. Any potentially relevant papers identied went through the same exclusion
and inclusion criteria. In total 24 additional papers were identied following this snowballing process, increasing
the number of selected papers to 126. The results of the process of searching for and identifying the nal selected
papers are shown in Figure 1.
ACM Comput. Surv.
A Survey of User Perspectives on Security and Privacy in a Home Networking Environment • 11
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
0
10
20
30
6040156
14 18 22 27 23
6040156
14 18 22 27 23
Fig. 2. The number of papers published yearly between 2010 and 2021.
4.2.2 Yearly Trend of Selected Papers. Figure 2 demonstrates the yearly trend of selected papers in the past 11
years (2010–2021). As can be seen, the majority of papers were published after 2016, indicating the fact that
related research has gained momentum towards the second half of the 2010s. This trend is not surprising given
the fact that smart home devices have become more popular more recently, and privacy and security issues
around them has become more prominent in the past a few years.
4.2.3 Thematic Analysis of Selected Papers. In order to answer the nine RQs dened for the SLR, we conducted a
thematic analysis of all selected papers and classied them into nine topical themes each corresponding to an
RQ, as shown in Table 6 (2016–2021) and Table 7 (2010–2015). Considering the overlaps between RQ3, RQ4 and
RQ5, we mapped selected papers to them as follows: papers that explicitly refer to home users’ understanding,
awareness, perception, knowledge and belief on security and privacy matters in the context of a home network
were categorized under RQ3; papers that have an explicit coverage on home users’ concerns or worried on
security and privacy issues and problems were categorized under RQ4; and papers that cover home users’ actual
security and privacy behaviors and practices were categorized under RQ5. Comparing papers published in the
past six years (2016–2021) and the earlier six years (2010–2015), we can see two revealing patterns: 1) research on
related topic has been increasing drastically recently since 2016; and 2) recent papers frequently cover multiple
RQs compare to earlier papers, indicating that more researchers realized the complexity of security and privacy
issues of home networks and the need to study them from multiple angles. We represented the inclusion of this
complexity in the included papers by the following specic graphic symbols: (a) “Substantial coverage” – to
represent papers which provided a substantial coverage of one RQ, (b) “Partial coverage” – when a part of the
paper is devoted to one RQ, and nally (c) “ Light touch“ – where a paper discussed about an RQ only briey.
Discussions for each RQ mainly concentrated on the papers which provided a full coverage, while mentioning
about the partial coverage. Across all papers published in the 11 years our SLR covered, we can see some popular
research areas (e.g., RQ4 “Privacy concerns”, RQ5 “Security behaviors and practices”, and RQ9 “Theoretical and
conceptual frameworks”). In addition, the results also revealed some obviously less-studied areas, especially
under RQ2 on multiple inter-connected devices and RQ8 on contextual aspects.
For each RQ, we will discuss the relevant papers covered in our SLR with greater details later, grouping them
into dierent sub-categories within each RQ. Table 8 shows an overview of all the papers which have been
mapped to the RQs and the sub-categories within each RQ.
4.2.4 RQ1: Holistic view.For this RQ, we considered 11 papers that examined security and privacy of home
networks from a more holistic point of view.
Papers in this category discussed dierent aspects, including privacy and security risks of users while adopting
to smart devices at home [
22
,
110
,
141
], general challenges for home users and solution [
37
], comprehensively
assessing privacy risks of smart home by investigating the data-collecting capabilities of its integral components
and assessing the individual risk they pose [
170
], general benets and risks [
113
,
191
], the role of trust [
56
,
162
],
and hybrid nature of modern smart homes [
34
,
136
,
201
]. The last two aspects are of particular interest for RQ1,
so we briey introduce the ve papers below with greater details.
ACM Comput. Surv.
12 • Nandita Panaik, Shujun Li, and Jason R.C. Nurse
Table 6. Papers in 9 RQs (2016–2021). Legend: Substantial coverage Partial coverage Light touch
Period Paper(s) RQ1 RQ2 RQ3 RQ4 RQ5 RQ6 RQ7 RQ8 RQ9
[119, 166]
[4, 77, 199]
[77]
[20, 120, 129]
[22]
[31]
[85]
[196]
[34]
[37, 137, 162, 170, 201]
[30, 40]
[42, 49]
[48, 100, 106, 136, 138, 174, 179]
[53, 146]
[1, 27, 67, 76, 93, 95, 121, 145, 163]
[56]
[57, 87]
[58]
[83]
[47, 61, 135, 164, 167, 183]
[7, 13, 46, 60, 71, 112, 154, 165, 171, 203]
[63, 84]
[72]
[81]
[82]
[28, 29]
[64, 92]
[94]
[4, 98, 188, 202]
[104]
[105]
[103]
[110]
[113]
[70, 114, 150]
[144]
[116, 118]
[122]
[126]
[134]
[141]
[9, 10, 68, 130, 130, 149, 192]
[153]
[173]
[178, 181, 187, 195]
[117, 197]
[199]
[47]
[59, 143]
11 5 34 46 63 16 17 8 21
2016–2021
Number of Papers
ACM Comput. Surv.
A Survey of User Perspectives on Security and Privacy in a Home Networking Environment • 13
Table 7. Categorization of papers into the 9 RQs (2010–2015). The same legend as in Table 6.
Period Paper(s) RQ1 RQ2 RQ3 RQ4 RQ5 RQ6 RQ7 RQ8 RQ9
[7]
[18]
[86, 88, 97, 176, 193]
[108]
[79, 86, 139, 186]
[189]
[190]
015150602
2010–2015
Number of Papers
The role of trust: Ferraris et al
.
[
56
] explored the trust relationship between the user and popular smart
devices and suggested an interesting trust model, which would enhance home security in regards to how devices
interact with users and other devices. Schomakers et al
.
[
162
] discussed how the degree of automation can aect
the privacy and trust perception of smart home users by not only exploring privacy from the information security
viewpoint but also reecting on the physical, social and psychological dimensions.
The hybrid nature of modern smart homes: Zhao [
201
] reected on the legal denition for the current
digital home which has a uid boundary and discussed some rising problems such as the changing perception of the
home’s location, the increasing signicance of data protection in the home, and the weakening legal enforcement
owing to the ‘cross border data-ows’ and ‘complicated industrial supply chain’. Nthala and Flechais [
136
]
investigated data security decisions in the home and proposed to study them by considering the home space in
three distinct areas: social, activity-based, and technological spaces. Boussard et al
.
[
34
] proposed the concepts of
‘vPlace’ and ‘vSpace’, to address the problem of proliferation of poorly secured IoT devices at home. ‘vPlace’ is the
collection of all registered resources in the home network and external devices owned by home users along with
their dynamic states of connection to the network of connected or not connected to the network, and ‘vSpace’
refers to virtual spaces or rooms oriented towards dierent contexts of family, work (from home) or visitors.
Some recommended solutions proposed in research falling into this category are interesting, such as calculating
privacy risks from the data collection capabilities of a device and dividing the whole home network into physical
and virtual places. However, we felt that research should take into account co-existence of dierent (both smart
and traditional) devices in a home network, exploring whether people’s security and privacy behaviors and
actions are dierent in dierent types of home networks such as in a student accommodation, an Airbnb or a
shared at.
4.2.5 RQ2: Multiple inter-connected devices.For this theme, we found only a small number of (four)
papers [7, 34, 119, 166], which are briey introduced below.
Al Abdulwahid et al
.
[
7
] focused on the unavailability of a common authentication technique using multiple
digital devices. Boussard et al
.
[
34
]’s vPlace and vSpace concepts, introduced previously for RQ1, utilized the
software-dened network (SDN) technique to manage access control in a multi-user home network with multiple
devices. Sikder et al
.
[
166
] discussed the complex and conicting demands of multiple users in a multi-device
home compared to a single-users environments and suggested a new access control system called Kratos to
enhance awareness of related environment. Marky et al
.
[
119
] (lab-based study,
�=
15) found that users would
prefer to have detailed information about each device, a clear status communication, more dynamic and rule-based
settings, and delegation options to adjust privacy settings in a multi-device setting.
The limited research on RQ2 suggests the need for research in the eld of interconnections and interactions
between multiple home devices, and any consequential security and privacy problems.
ACM Comput. Surv.
14 • Nandita Panaik, Shujun Li, and Jason R.C. Nurse
Table 8. A taxonomic view of the research papers under dierent sub-categories belonging to the RQs
RQ Sub-category Paper(s) #
RQ1: Holistic View
Smart device adoption [22, 110, 141] 3
General challenges & solutions [37] 1
General benets & risks [113, 191] 1
Data ows & privacy risks [170] 1
The role of trust [56, 162] 2
Hybrid nature of modern smart home [34, 136, 201] 3
RQ2: Multiple Devices
Lack of common authentication techniques [7] 1
Multi-device and multi-user scenarios [34, 166] 2
Multi-device privacy conguration [119] 1
RQ3: User Awareness
Eect of workplace training [88, 97, 121, 176] 4
Privacy awareness & perception [27, 31, 53, 58, 67, 95, 116–118, 146, 162, 188, 199, 202] 14
Smart personal assistants [1, 85, 98, 113, 145] 5
Dierent types of home users [5, 93, 193] 3
PrivSec awareness in general [57, 83, 87, 103, 105, 110, 113, 196, 197] 9
RQ4: User Concern
Privacy concerns in general [13, 83, 104, 150, 192, 196, 203] 7
Concerns with smart speakers [42, 46, 60, 82, 85, 103, 112–114, 130, 144] 11
Reection on underlying behaviors [68, 108] 2
Parental privacy concerns [15, 149, 171] 3
Other aspects [9, 22, 40, 47, 49, 70–72, 81, 105, 154, 165, 190] 13
PrivSec concern in general [9, 28, 29, 59, 59, 88, 134, 141, 153, 197, 199] 11
RQ5: Behavior/Practice
Security practice related decision [9, 136–138] 4
Management and conguration [79, 87, 179] 3
Security behavior aecting practice [18, 31, 53, 58, 77, 174, 192] 7
Personalized approaches [57, 186] 2
Privacy-specic behaviors [8, 42, 49, 103, 113] 5
Machine Learning based study [22, 106, 114, 134] 4
Other aspects [3, 86, 139, 149] 4
PrivSec behavior in general
[
4
,
47
,
58
,
61
,
63
,
68
,
70
,
84
,
98
,
100
,
103
,
105
,
114
,
116
,
118
,
123
,
126
,
130
,
134
,
135
,
141
,
144
,
146
,
150
,
164
,
167
,
173
,
178
,
181
,
183
,
187
,
188
,
195
,
200
,
202
]
35
RQ6: Multi-User Bystander privacy [4, 28, 29, 48, 119, 197] 6
Access control & conguration [63, 77, 84, 119, 200] 5
Devices shared by multiple users [63, 82, 84, 103, 113, 117, 118] 7
RQ7: Demography
Gender [61, 104, 123, 135, 189] 5
Non-gender demographic factors [30, 40, 47, 94, 153, 167, 183, 186, 190] 9
Location [81, 100, 164] 3
RQ8: Contextual Location as context [120, 129, 134] 3
Device as context [20, 77, 105, 112, 122, 173] 6
RQ9: Theoretical
Protection Motivation Theory (PMT) [18, 53, 64, 122, 126, 187] 6
Extended PMT [178, 181] 2
Theory of Planned behavior (TPB) [56, 72, 195] 3
Conceptual Framework [59, 92, 104, 108, 141, 143, 144] 7
4.2.6 RQ3: Security and privacy awareness of home users.35 papers covered in our SLR studied users’
awareness and perception of security and privacy in the context of home networking, with 24 papers substantially
covering this topic.
The eect of workplace training and the subsequent cyber awareness for home users is a topic that has
received some attention. While some researchers investigated organizational, social and personal factors that can
aect cyber security awareness of home users and attributed the cyber awareness of the home users to personal
initiatives’ knowledge, others such as Talib et al
.
[
176
] and Kritzinger and von Solms [
97
] recommended organized
ACM Comput. Surv.
A Survey of User Perspectives on Security and Privacy in a Home Networking Environment • 15
security awareness programs at the workplace to help boost home users’ cyber awareness. Kang et al
.
[
88
] and
McDermott et al
.
[
121
] suggested the absence of a direct relationship between people’s technical background and
their security or privacy awareness.
The topic of low privacy awareness is another popular topic discussed by many researchers [
53
,
67
,
116
–
118
,
146
,
163
,
202
], owing to reasons such as the absence of audio-visual inputs [
202
] or a low level of self-
ecacy [
146
]. On the positive side, it was also reported that home users are willing to engage in privacy
protective mechanism, if relevant tools are easy to understand and cheap [
53
]. Through an online role-playing
exercise, Binns et al
.
[
31
] (online role playing scenario,
�=
27), found that home users’ privacy-related decisions
are heavily inuenced by their pre-existing perceptions of and relationships with companies (more precisely,
mobile app suppliers). They suggested privacy-aware tools that can help users to incorporate such pre-existing
contextual factors into their privacy-related decisions. Two studies [
27
,
95
] suggested using augmented reality
related solutions to make home users aware of privacy issues and to encourage them to make more informed
decisions. Wickramasinghe and Reinhardt [
188
] (survey,
�=
229), observed that users had a lack of knowledge of
sensitive data collected by smart objects and the thirds parties receiving such data. Zeng et al
.
[
199
] (interview,
�=
15), suggested how users’ vulnerability depends on the level of their privacy knowledge, and pointed out a
clear mismatch between the awareness and power of the owner/administrator of the smart home in comparison
to other home users. Freudenreich et al
.
[
58
] (interview,
�=
16), studied security practices of home users related
to Wi-Fi security and found that, although they were mostly aware of the vulnerabilities, they found it dicult to
address these issues.
Due to the growing popularity of smart speakers (also called smart/intelligent personal assistants or voice
assistants – we will use the shorter term “smart speaker” hereinafter), some studies [
1
,
85
,
113
] looked into
dierent security and privacy aspects of smart speakers. They found that, home users were generally aware of
personal data being stored on smart speakers, but not with the service provider and in some cases with other third
parties. Most users were not even aware that they could review or delete the stored data [
113
]. The incomplete
mental model on dierent privacy issues [
1
] or lack of knowledge on the matter [
98
] leads to various privacy
concerns. Park and Lim’s research [
145
] demonstrated that at times, when users are more aware of their privacy,
they expected their own personal space oriented features in the smart speakers.
Three papers looked at dierent types of home users. Kim et al
.
[
93
] (trend analysis
�=
23; interview
�=
20; survey
�=
188) categorized users into dierent groups depending on the level of their cyber awareness.
For example, ‘Innocent Irene’ (extreme low level of awareness) or ‘Parental Patrick’ (people with a family to
protect). They suggested designing smart devices to suit to home users’ cyber personality. Xavier and Pati [
193
]
(survey,
�=
324) pointed out how lack of awareness aects the ability to understand security threats whereas
Ahmad et al. [5] focused on lack of parental awareness on the topic of cyber threat towards children at home.
Home user awareness and perception was also addressed in nine papers [
57
,
83
,
87
,
103
,
105
,
110
,
113
,
196
,
197
],
while focusing on other related concepts such as privacy and security concerns, privacy and security behaviors
of single and multi-users and contextual security at home.
Research on RQ3 has a noticeable focus on privacy awareness, possibly because privacy issues of smart
devices such as smart speakers are more visible and understandable by home users than (technical) security
issues. The research on the eect of workforce training was a topic we did not expect, showing how home and
work contexts can be connected. Discussions on user awareness on topics such as issues from connected home,
existing tools and supports to mitigate issues arising, understanding of home user on legal and economical help
available in case of security breach are surprisingly thin and important areas to focus on.
4.2.7 RQ4: Security and privacy concerns of home users.RQ4 is one of the most discussed themes,
with 47 papers covering a range of related topics, with 36 of them focusing majorly on this RQ. The other 11
ACM Comput. Surv.
16 • Nandita Panaik, Shujun Li, and Jason R.C. Nurse
papers [
9
,
28
,
29
,
59
,
88
,
134
,
141
,
143
,
153
,
197
,
199
] focused more on topics in other RQs such as multi-user
concept or user behaviors, so they will be covered elsewhere.
Seven papers looked at privacy concerns in a more broader and systematic sense. In order to understand
the privacy concerns of home users, Zimmermann et al
.
[
203
] (interview
�=
42) categorized home user concerns
under two types, ‘unrelated to attacks’ such as dependency on a technology or loss of control to use, and ‘related
to attacks’ such as smart home data exposure and manipulation of device sensors. Worthy et al
.
[
192
] (experiment,
�=
5) collected daily use data from the participated homes with a specially developed IoT device, to nd that
that users tend to demand more control over the information collection process when they have less trust on
the data controller and consumers. Through an empirical analysis involving 265 valid respondents, Lee [
104
]
(survey,
�=
300) observed a positive association between dierent types of vulnerabilities (‘Technological’,
‘Legal’ and ‘User’) and IoT privacy concern, except provider vulnerabilities. Hwang et al
.
[
83
] (survey,
�=
300)
revealed dierent levels of privacy risk perception for dierent types of home IoT services, venturing into a
trade-o between privacy and functionalities. This trade-o is also re-iterated by a study conducted by Psychoula
et al. [150](survey �=231; interview �=41). Yao et al. [196] explored user-centered privacy design (Co-design
study,
�=
25) to demonstrate home users’ conceptualization of privacy control mechanisms. They identied six
factors (Data Transparency & Control’, ‘Security’, ‘safety’, ‘Usability’, ‘Contextual detection & Personalizing’, and
‘System Modality’) to help design smart home privacy controls. A study [
13
] focused on understanding Saudi
home users’ privacy and security concerns of using smart devices, and revealed that 79.7% of Saudi users were
afraid of losing their data because of low awareness of related issues and the lack of governmental interventions.
Eleven papers discussed privacy concerns on smart speakers, the most popular topic under RQ4, and
discussed it from many angles, i.e., lack of privacy concerns by users, contextual privacy concerns, data
collection behaviors of the manufacturers, the underlying behaviors such as trust and psychological
factors. Lau et al
.
[
103
] conducted a diary study with 34 smart speaker user and non-users, to nd that smart
speaker users lacked knowledge on privacy risks whereas non-users lacked trust on the vendors. This was further
highlighted by Malkin et al
.
[
113
] and Chalhoub and Flechais [
42
], who commented on how users preferred
comfort to privacy because of the low level of privacy concern and any desire to observe privacy behaviors
is inhibited by the lack of user-friendly interface. Concerns about the privacy risks that smart speakers can
track and listen to users’ data were observed in two studies [
46
,
85
]. Fruchter and Liccardi [
60
] used dierent
NLP (natural language processing) tools to process 109,536 online user reviews on Amazon Echo and Google
Home for the presence of specic security and privacy-oriented keywords. Only 2% of the reviews using those
keywords showed major concerns about data collection. Manikonda et al
.
[
114
] examined online reviews to
note users’ positive outlook towards the smart speakers and a good level of privacy. Mols et al
.
’s explorative
study (survey,
�=
325; focus group,
�=
35) on the Dutch households’ privacy concerns [
130
] provided a
multi-dimensional understanding of users’ concerns including surveillance, device security, day-to-day user
behaviors and transparency of platforms. Lutz and Newlands [
112
] (survey,
�=
325) discussed privacy concerns
related to smart speakers from a contextual perspective, suggesting that such concerns vary depending on
the source. Two papers focused on users’ concerns about the data collection behaviors of smart speaker
manufacturers. Huang et al
.
[
82
] investigated shared use of smart speakers (interviews,
�=
26), and observed
that participants expressed privacy concerns about their housemates and visitors, and also about privacy-invasive
data collection by speaker manufacturers. Park et al
.
[
144
] (survey,
�=
359) noticed that privacy concerns led to
negative privacy-coping behaviors such as anger, anxiety and disappointment against the relevant companies,
and generate bad words of mouth or disengagement.
Reection on the underlying behaviors such as psychological and trust behind users’ concerns about
smart speakers was explored in two papers. Liao et al
.
’s investigation (survey,
�=
1160) on the role of privacy
and trust in users’ decisions on adopting smart speakers [
110
] revealed that, users tend to trust the vendors
(Google, Amazon, and Apple) on the usage of their information, and privacy concerns dier between people
ACM Comput. Surv.
A Survey of User Perspectives on Security and Privacy in a Home Networking Environment • 17
who use dierent hardware devices (a smart phone or a smart speaker) to interact with the agent. Ghosh and
Eastin [
68
] (survey,
�=
289) examined dierent psychological mechanisms underlying home user’s interaction
with software agents such as Alexa and Siri and hardware devices such as smart speakers and smart phones, and
how they aect privacy concerns and information disclosure behaviors. Similar to what Ghosh and Eastin [
68
]’s
study explained about how participants are more likely to report on higher privacy concerns when interacting
with voice assistants through smart speakers than through smart phones.
Parental privacy concern was discussed in three papers. Reecting on parent’s technology usage, control
and concerns, Alqhatani and Lipford [
15
] (interview,
�=
20) stated that parents did discuss children’s privacy
and security concerns regarding their online use and controls but did not expand on these to include smart
devices. In contrast, Prasad et al
.
[
149
] (interview,
�=
20) considered parental privacy concerns towards service
providers and manufacturers and how they aect their children. Sun et al
.
[
171
] (interview,
�=
23) identied
six factors which, according to them, inuence parents’ perception of privacy risks, including parenting style,
tech-savviness, trust in manufacturer, age of the children, features of the used devices and news media reporting.
While twelve papers considered other aspects which might aect privacy concerns of home users.
Golbeck [
70
] analyzed 501 online comments to nd out that 81% of the home user have concerns about using
their Internet Service Providers (ISP) supplied home router as a public Wi-Fi hot-spot. Privacy concern was
found to be the most prevalent ethical concern in a study (survey,
�=
631) by Seymour et al
.
[
165
]. Through
their study on authentication management techniques of home users (
�=
93), Alam et al
.
[
9
] revealed that this
type of privacy concerns does not actually reect users’ practices. Grünewald and Reisch [
71
]’s study (survey,
�=
701) revealed that participants were more inclined to share their location data for services (50%), than with
service providers (28%), but struggled with dierentiating between the two. Lee and Kobsa [
105
] conducted a
clustering analysis based on data collected from 200 participants on 2,800 hypothetical IoT scenarios and ve
contextual parameters (‘where’, ‘what’, ‘who’, ‘reason’ and ‘persistence’) aecting home users’ privacy concerns,
to nd scenarios according to their privacy risks. A new theoretical model (which will be discussed more in
RQ9) was suggested by Guhr et al
.
[
72
] to nd out the role, privacy concerns plays in home users’ acceptance of
smart home technology. Results of Crabtree et al
.
’s research [
49
] demonstrated the fact that the discussion and
design eorts for any sort of privacy mechanisms should focus on managing human relationships rather than
controlling data ows. Both Barbosa et al
.
[
22
] and Cannizzaro et al
.
[
40
] commented on users’ reluctance on
home IoT adoption due to their privacy concerns, and how such reluctance grows with age [
40
]. Devices and
platforms other than smart devices such as ambient assisted living (AAL) [
47
], mobile-assisted robots [
154
] were
also studied to understand the type of privacy concerns they might generate. Wilkowska et al
.
[
190
] used three
dierent methodologies, i.e., a focus group(
�=
42), a survey(
�=
104) and an experimental usability study(
�=
55),
to study home users’ privacy concerns in the context of AAL and found that the level of privacy concerns of
participants observed, following each methodology diered signicantly.
Finally, one paper paid attention to the role of cultural background in privacy concerns: Huang and Wu [
81
]
conducted a small interview study with nine Chinese smart home users, showing some preliminary evidence
regarding their privacy concerns likely being less than an average American user (based on past American
privacy-related studies).
Multiple papers related to RQ4 focused on a specic type of smart devices – smart speakers. The trade-os
between privacy and functionality, how privacy concerns aected the decision of smart home adoption, are also
relatively popular topics of discussion. However, we found that some smart home devices such as smart
home appliances are much less studied, despite the increasing use of these devices in modern smart homes.
4.2.8 RQ5: Security & privacy behaviors and practices of home users.This is another popular area of
research, with 66 papers. Some papers (32) focused majorly on this RQ and others (34) reected on security and
privacy behaviors while discussing other concepts such as privacy concerns, awareness, multi-users issues and
ACM Comput. Surv.
18 • Nandita Panaik, Shujun Li, and Jason R.C. Nurse
related conceptual frameworks [
4
,
47
,
58
,
61
,
63
,
68
,
70
,
84
,
98
,
100
,
103
,
105
,
114
,
116
,
118
,
123
,
126
,
130
,
134
,
135
,
141
,
144
,
146
,
150
,
164
,
167
,
173
,
178
,
181
,
183
,
187
,
188
,
195
,
200
,
202
]. Here, we will focus on the 32 papers in the
former group.
Security practice related decision was discussed in four papers. Nthala and Flechais [
136
] in their study,
(interview,
�=
15) identied four themes ( stimuli (cues to action), support, stakeholders, and context) around
home users’ security practice related decisions. In a follow-up work (interviews,
�=
50) [
138
], they found that
home users majorly rely on family and friends as an informal support network. Furthermore, in another follow-up
work (survey,
�=
1
,
128; interview,
�=
65) [
137
], they found that security practice is aected by survival/outcome
bias, factors undermining condence in a security measure, in addition to other well known factors such as trust,
cost, knowledge and skill. Alam et al
.
[
9
] observed that despite being very concerned about their privacy and
security, users don’t follow appropriate security steps.
Management and conguration of smart devices was explored in three papers. Kaaz et al
.
[
87
] (experiment,
�=
7) found that, contrary to the popular belief, smart devices are not ‘plug and play’ and a majority of home
users face multiple barriers while conguring such devices, which often force them to accept vendors’ default
(possibly awed) security and privacy settings. Ho et al
.
[
79
], in an earlier (2010) study on home wireless networks,
revealed how home users depended on out-of-box security tools provided by manufacturers, hardly changed the
default security settings, rarely installed and maintained encryption keys across devices, and did not perceive
any dierences between encryption and access control. Topa and Karyda [
179
] collected and analyzed a broad
spectrum of privacy and security issues on usability of security tools such as VPNs, anti-virus and anti-spyware
programs (scenario-based, survey,
�=
150; interview,
�=
112) to highlight the need of detailed help, consistency
regarding use of technical terms, concerns over the use of personal data, and the absence of appropriate usability
tools for disabled users.
Seven studies looked at security behaviors aecting home users’ security practices for home networks.
Anderson and Agarwal [
18
] observed (survey,
�=
600) that home users’ security behaviors were inuenced by
cognitive, social and psychological components and hence, all such factors should be considered when analyzing
their security attitudes. He et al
.
[
77
] (survey,
�=
425) conrmed a proposed hypothesis that, participants focused
on IoT devices’ capabilities rather than the devices themselves to dene access control and authentication policies.
Binns et al
.
[
31
] concluded that, home users’ security actions are mostly inuenced by their preconceived notion
about the company responsible for the specic hardware device or software (e.g., a mobile app). However, Dupuis
and Ebenezer [
53
] were of the opinion that home users would be more willing to take precautionary actions if
they clearly understand the security mechanisms of the products they use. Via a mixed-method study (survey,
�=
1
,
006; interview,
�=
14), Taieb and Pelet [
174
] observed that home users’ attitudes and perception towards
IoT devices and the security of their data depend on the device type, e.g., a smart health device could induce them
to share more information than a smart speaker. Worthy et al
.
[
192
] conducted an experiment to collect data
from ve dierent households for a period of 10-14 days and analyzed the inhabitants’ behaviors. They observed
that the participants became familiar with new devices very soon and were generally happy with sharing data
as long as it is properly de-identied (e.g., via aggregation) and they were aware of the purpose of collection.
Risks associated with Wi-Fi vulnerabilities in home users were explored by Freudenreich et al
.
[
58
] (survey and
interview,
�=
16), who found that, although generally aware of the privacy risks, people were not knowledgeable
or skilled to address such problems.
The need of personalized approaches is highlighted in some work. Both Wash and Rader [
186
] and Forget
et al
.
[
57
] argued on the unsuitability of ‘one size ts all’ approach to security. Wash and Rader [
186
] discussed
dierent ‘folk models’ of threats that induce a home user to make dierent security decisions according to their
contextual belief. Whereas Forget et al
.
[
57
] collected user security behaviors and machine congurations of
73 users for at least 3 months within 9-month time window, and then interviewed 15 users out of the 73 ones.
Their results reiterated a nding of Wash and Rader [
186
] – since home users engage in security behaviors and
ACM Comput. Surv.
A Survey of User Perspectives on Security and Privacy in a Home Networking Environment • 19
practices dierently, they may benet from dierent styles and dierent levels of interventions for their specic
needs.
privacy-specic behaviors of home users were explored in several papers. Crabtree et al
.
[
49
] studied
digital practices of 20 homes in the UK and found that participants employs a number of ‘ne-grained meth-
ods’(throwaway emails, ad blockers, cookies, consent forms, private browsing) to manage the ow of their private
data securely. Chalhoub and Flechais [
42
] investigated home users’ attitude in terms of the user experience
(UX), and pointed out that the lack of users’ privacy concerns arose out of their individual perception of the
situation and how they traded their privacy needs for the benets from smart devices. Lau et al
.
[
103
] and Malkin
et al
.
[
113
] found no evidence of privacy-seeking behaviors in users of smart speakers, and observed that users
did not use privacy controls already available to them in such devices. Al-Ameen et al
.
[
8
] revealed a number of
mismatches between users’ actual perception of data collection and data sharing by the IoT devices compared to
the devices’ published privacy policies.
Four papers used machine learning(ML) tools to reect on home users’ privacy or security behaviors. Naeini
et al
.
[
134
] used ML classiers to predict users’ preferred comfort level and their decision to allow or deny specic
data collection, whereas Barbosa et al
.
[
22
] implemented a decision tree classier to suggest how easy aordability
as a ‘motivator’ can defocus privacy as a ‘blocker’. Manikonda et al
.
[
114
] applied the latent Dirichlet allocation
(LDA) algorithm [
32
] and Word2Vec [
125
] to understand users’ privacy behavior and concerns. Li et al
.
[
106
]
analyzed home users’ security and privacy behavior by examining 15.4 million video streams from 211k Chinese
users and observed that frequent use of the camera increases the privacy risks.
Four papers looked at other aspects of user attitudes, perceptions and behaviors. Prasad et al
.
[
149
] discovered
(focus groups,
�=
3; interviews,
�=
14) that parents did not trust device/software manufacturers or ISPs to
protect their children from harms when using smart devices, and felt it was their responsibility to do so. Jin
et al
.
[
86
] discussed a dierent topic of residential privacy by examining data collected from the Foursquare
application and identied several vulnerabilities and privacy risks caused by user behaviors. Oulasvirta et al
.
[
139
]
used a specic behavioral observation system (BOB) to pool sensor data from designated surveillance devices
(Wi-Fi cameras, key-presses from personal computers, smart devices, TV and DVD media centers) to comment on
the contextual behavioral change. Abrokwa et al
.
[
3
] (survey,
�=
493) observed no signicant privacy behavioral
dierences between users of two mobile operating systems (iOS and Android).
The RQ5-related papers cover a wide range of topics, e.g., home users’ privacy and security behaviors, practices
and decisions, and parental behaviors, which mostly focused on issues around standalone devices. As in the case
of RQ3 and RQ4, more future research should focus on understanding home users’ behaviors and attitudes in
a connected home with hybrid devices.
4.2.9 RQ6: Multiple users in a single home.Eighteen papers in our study covered security and privacy
issues in a multi-user home environment, in dierent levels of depth.
Five papers examined privacy of bystanders, e.g., guests and nannies. Yao et al
.
[
197
] examined potential
privacy concerns and expectations of the bystanders (Focus groups,
�=
18) and observed strong contextual
variations, as they switch their roles under dierent social relationships. Bernd et al
.
[
29
] identied dierent
types of bystanders including nannies, home care attendants, house cleaners and maintenance workers who can
be aected by the use of smart devices but are not directly involved in the use of such devices. In a follow-up
study, Bernd et al
.
[
28
] (interview,
�=
25) found that nannies expected the existence of smart cameras but wanted
transparency of information from their employer (i.e., the homeowner) beforehand and were concerned by
potential misuse of collected data. Cobb et al
.
[
48
] surveyed 386 incidental users of smart devices, to understand
their most typical concerns and the context where it materializes, and recommended better communication
between the primary and incidental users. Ahmad et al
.
[
4
] (interview,
�=
19), proposed a concept of ‘tangible
privacy’ for designing IoT devices, to provide stronger privacy assurances to bystanders.
ACM Comput. Surv.
20 • Nandita Panaik, Shujun Li, and Jason R.C. Nurse
Four other papers discussed challenges in a multi-user home, focusing on access control and conguration
managements of home devices by dierent users in the same home environment. Access control issues in
a multi-user home were discussed by three groups of researchers [
77
,
84
,
200
], who conducted scenario-based
analyses and discussed challenges of using smart devices in a multi-user environment, including coarse-grained
access control resulting in either complete access or no access to users, intended or unintended threats to the
primary user’s data. Marky et al
.
[
119
] investigated a prototype for multi-setting interface to adjust privacy
settings by multiple users with multiple devices (experiment,
�=
15) and found that users prefer ability to
access detailed information with the settings. By exploring security and privacy implications in a multi-user
home, Zeng and Roesner [
200
] designed a prototype with dierent access control features (i.e., location-based,
supervisory). They discovered that factors such as usability and conguration complexity, lack of concerns for
devices, interference with other functionalities, trust between dierent home users are some of the reasons why
users ignore access control mechanisms. Four researchers [
77
,
84
,
200
] recommended dierent design changes
in smart devices to increase the usability and accessibility of the functionalities to all home users. Additionally,
He et al
.
[
77
] exploring dierent types of relationship in a multi-user home (‘Babysitter vs. visiting family’, and
‘Child vs. teenager’) found clear dierences in dierent users’ desires to have specic access control policies
attached to dierent capabilities of an IoT device.
Nine researchers discussed the challenges of device use in a multi-user home by primary and secondary
users, mostly focusing on the shared use of smart speaker. Both Malkin et al
.
[
113
] and Lau et al
.
[
103
] reected
on privacy tensions between primary, secondary and incidental users of smart speakers, while Zeng et al
.
[
199
]
pointed out unique privacy and security challenges that occur in a multi-user home where incidental users
depend on the primary users’ knowledge and control.In a related, study (interview,
�=
21),Marky et al
.
[
116
]
observed that visitors would usually accept the data collection by smart devices so long as the data is anonymized
and recommended to gain awareness and knowledge and evaluate data sensitivity, to exert control over their
privacy. In a follow-up study, Marky et al
.
[
118
] (interview,
�=
42) investigated two related privacy issues –
privacy of bystanders to homeowners and privacy of homeowners to bystanders. They recommended that, the
IoT designers must pay attention to both bystanders and the users while designing their devices. In their study
on mental model of 30 participants, Marky et al
.
[
117
] noticed a general lack of awareness amongst visitors about
the data ows in a smart home ecosystem. Based on interview data from 26 participants using smart speakers,
Huang et al
.
[
82
] (interview,
�=
26) found that participants had dierent types of concerns about inappropriate
access and misuse of personal information by housemates and other external entities, but would follow the same
risk management strategies in both cases. Geeng and Roesner [
63
] conducted a mixed-method study (
�=
18) to
study the inter-communication, tensions, and challenges in a multi-user home. They observed that the smart
home environment mimics the existing power dynamics (i.e., parent-child) in a household, giving smart home
drivers more access to functionalities than other users. Park and Lim [
145
] discussed on the privacy awareness of
family members while sharing a smart speaker.
Papers related to RQ6 cover mainly two areas, bystander privacy and access control. Users’ understanding
of the more complicated data ows in a multi-user home is one of the main topic that we felt should be
studied more, along with other areas such as threats from malicious secondary users and security and
privacy implications of interactions between multiple devices and multiple users.
4.2.10 RQ7: Demographic factors and their eects.Seventeen papers in our study substantially covered
topics related to this RQ.
Five papers studied home users’ security behaviors, practices and concerns, with a focus on gender. Wilkowska
and Ziee [
189
] studied the use of e-health technologies at home (
�=
104, 60 females and 44 males), showing
that female and healthy adults were more prone to demand stringent security and privacy standards than male
adults and ailing elderly, respectively. In contrast, Nohlberg and Kävrestad’s survey (152 participants, 53 females
ACM Comput. Surv.
A Survey of User Perspectives on Security and Privacy in a Home Networking Environment • 21
and 99 males) [
135
] found men to be more decisive in comparison to women in an information security decision.
McGill and Thompson [
123
] surveyed 624 users (234 females and 390 males) and their results echoed Nohlberg
and Kävrestad’s nding that security behaviors of female users are weaker than male users’. Furini et al
.
[
61
]
conducted a small study during the 2020 COVID-19 lockdown on people’s privacy behaviors and concerns on
smart speakers, and found that both male and female users had privacy concerns. Looking into gender and IoT
use experience, Lee [
104
] concluded that female users were more concerned by their own vulnerabilities and
people without technical experience were more concerned by providers’ vulnerabilities.
Some other papers focused on demographic factors beyond gender such as age and disability while analyzing
privacy and security attitudes and concerns of people using AAL, older adults and other variables such as age,
ethnicity, income level and disability. van Heek et al
.
[
183
] studied the acceptance of AAL technologies, and the
trade-os between perceived benets and barriers (survey,
�=
279). They found that user diversity in terms of
age, disability and care giving experience does signicantly aect the trade-os between perceived benets and
barriers. In another related study that used three dierent methodological approaches (focus groups, survey and
a usability study), Wilkowska et al
.
[
190
] noticed privacy with regard to AAL technologies is independent of the
age or gender. The same conclusion was also drawn by Choukou et al
.
[
47
], who compared the attitude of older
and younger adults on the use of AAL technology. However, when age was discussed as a factor inuencing
privacy and security attitude in general (not specic to AAL), it tends to play a signicant role in user attitude.
Age was a parameter studied by Singh et al
.
[
167
] (survey,
�=
231) who found that older adults (36 – 70) were
more willing to share data on health grounds than their younger (below 36) counterparts. In their analysis of a
large-scale survey with 2,033 UK participants, Cannizzaro et al
.
[
40
] noticed that age and education level play a
signicant role in determining people’s trust on IoT devices for security and privacy. In another study, Wash and
Rader [
186
] found that educated users and older adults often exercise fewer precautions in regard to security
threat. Klobas et al
.
[
94
] reported similar results about security perceptions of older and educated participants
who seemed to be more likely to assess security risks of IoT products and had a more positive attitude towards
them, although they were still concerned about the privacy and functionality, which does not meet the need of
their specic requirements. [
30
]. Reeder et al
.
[
153
] interviewed ten post-menopausal women (age band: 50-70) to
understand their perception of wearable devices and found that the participants largely accepted the technology
as useful.
Seidl et al
.
[
164
]’s study (survey,
�=
214) found that geo-privacy behaviors are very much linked to a
participant’s underlying knowledge of the eld and similar across dierent demographic factors including
gender, ethnicity and income level. Huang and Wu [
81
]’s comparative study on Chinese and US users revealed
that users’ privacy concerns in China seemed to be lower than in Western countries. Lafontaine et al
.
[
100
]
conducted a survey (
�=
232) over three geographic regions (the US, the EU and India) and found that IoT users
were comparatively comfortable in accepting risk than non-IoT users. Furthermore, they observed contrasting
behaviors of users in dierent regions, i.e., people in India trust their government more in protecting their data
compared to people in the US and the EU.
Papers related to RQ7 studied demographic factors such as gender, age, educational background, disability,
ethnicity and income level, and their implications on security and privacy behaviors of home users. There is
contradictory evidence of weaker security behaviors of females compared to males, therefore needing
more research in this area. We noticed that a majority of the studies focused on developed nations such as the
UK, the US and the EU, so more research on developing countries and non-Western countries is much needed.
4.2.11 RQ8: Contextual factors influencing security and privacy behaviors and practices at home.
Eleven papers in our study contributed substantially to this theme.
Three papers considered location as the contextual factor. McCreary et al
.
[
120
]’s study (video experiment,
�=
264) found that people were very much concerned about privacy inside their home regardless of the activities
ACM Comput. Surv.
22 • Nandita Panaik, Shujun Li, and Jason R.C. Nurse
they are involved in compared to outside their home. Molina et al
.
[
129
] (survey,
�=
276) asked their participants
to imagine using Wi-Fi networks at four dierent locations (coee shop, university, Airbnb, and home). One of
their hypotheses is that a higher belief in publicness heuristic can lead to less information disclosure. Their results
showed positive evidence to support this hypothesis. Naeini et al
.
[
134
]’s research (vignette study,
�=
1
,
007)
working with a set of 380 IoT data collection and dierent scenarios revealed the context-dependence and the
diverse nature of privacy preferences of home users.
Other researchers analyzed contextual use of devices [
139
] aecting privacy and security behaviors or
leading to specic individual knowledge and experience or device’s primary function dictating users’ privacy
perception [
4
]. Lee and Kobsa [
105
] conducted a comprehensive analysis using K-mode clustering analysis,
employing data from hypothetical IoT scenarios (survey,
�=
200). They used K-modes clustering analysis with
four clusters (‘Very unacceptable’, ‘Unacceptable’, ‘Somewhat acceptable’, and ‘Acceptable’) and analyzed the
data with ve contextual parameters (‘where’, ‘what’, ‘who’, ‘reason’ and ‘persistence’) to reect on the impacts
of contextual factors on peoples’ privacy perceptions. Apthorpe et al
.
[
20
] used the contextual integrity (CI)
framework (survey,
�=
1
,
731). They collected 3,849 information ows passing between various rst and third-
party recipients in a smart home to provide rich insights into why device manufacturers should survey privacy
norms in specic contexts and why privacy norms should support restrictive rather than permissive IoT device
communications. Tabassum et al
.
’s [
173
] study, (interview,
�=
23) discovered that users’ threat modelling of their
home and their protection behaviors were not shaped by their existing knowledge, but by their experience in
other computing contexts. McGill and Thompson’s research (survey,
�=
629) on users’ security behaviors [
122
]
revealed that users perceived to expect more risks from the use of a mobile device than from a home computer. He
et al
.
[
77
] studied access control issues in multi-user homes (
�=
425) and looked at frequent context-dependent
capabilities of various IoT devices. They noticed ve contextual factors (‘Age’, ‘Location of Device’, ‘Recent Usage
History’, ‘Time of Day’ and ‘Location of User’) impacted signicantly on the implementation of access control
capabilities of smart devices. According to Lutz and Newlands’s study [
112
], privacy concerns are dependent on
the context of the origin source.
The major areas of discussion on this RQ were location and use of smart devices. However, we feel that
some other important contexts need more research, including varying security behaviors and practices of home
users when using traditional devices verses smart devices, sharing a particular device with other users, dierent
types of smart devices other than more studied ones such as smart speakers. There is also a lack of research on
the legal context, e.g., home users’ understanding of the legal support available to them in case of any security or
privacy breaches, and their legal rights when it comes to the storage and manipulation of their data on dierent
types of home devices.
4.2.12 RQ9: Theoretical frameworks of security and privacy behaviors.With Eighteen papers in our
data set, RQ9 is another much-discussed theme in our study.
Seven papers in this theme [
18
,
53
,
64
,
94
,
122
,
126
,
187
] discussed users’ security behaviors in light of the PMT
(Protection Motivation Theory)[
156
]. Analyzing the survey data from 72 home computer users, Mills and
Sahi [
126
] concluded that participants were not signicantly inuenced by perceived vulnerability or perceived
severity when trying to implement additional security measures on their home computers. However, they
identied that response ecacy and self-ecacy were moderate predictors of individuals’ intention to implement
additional security measures. Klobas et al
.
[
94
] study on security risk’s inuence on smart home adoption (survey,
�=
405) observed the perceived risk as a determinant of smart home adoption intentions. White et al
.
[
187
]
used a survey with 945 adult participants to investigate dierent factors that aect computer security protective
behaviors and perceived security incidents. Dupuis and Ebenezer [
53
] used a mixed-method study under the
framework of PMT, using analysis of data from 500 customer reviews of ten IoT devices, 18 interviews, and
a large-scale AMT-based online survey with 1,006 valid response, to study the lack of privacy-risk awareness.
ACM Comput. Surv.
A Survey of User Perspectives on Security and Privacy in a Home Networking Environment • 23
They found that home users would engage in dierent privacy protected mechanisms if they are simple to
understand and cheap to use. George et al
.
[
64
] reiterated this fact in their survey (
�=
219), when they found
that low awareness of risk coupled with self-ecacy hinders the users from addressing the existing privacy risks.
Anderson and Agarwal [
18
] used PMT to examine security behaviors of 101 participants in a survey with 594
home computer users and an experiment with 101 participants. They concluded that users’ security behaviors
were inuenced by an individualized message focusing on the benets of good security behaviors.
Two Papers extended the functionalities of PMT for their investigation. Tsai et al
.
[
181
] examined how PMT
factors predict users’ security intentions (survey,
�=
988). They extended the original PMT theory by including
commonly neglected variables such as threat susceptibility, prior experience with a safety hazard, coping self-
ecacy, to understand threat perspectives of home computer users while being online. They found several factors
such as gender, age, threat severity, prior experience, coping self-ecacy, personal responsibilities amongst
others, that are signicantly co-related with users’ security intentions. Thompson et al
.
[
178
] (survey,
�=
629),
included the social and peer inuence, psychological ownership and metrics on actual behaviors to measure the
eectiveness of these factors on user behaviors under dierent contexts. Their results demonstrated that users
behaved dierently under both contexts (personal computers and mobile devices). Their ndings echoed the PMT
theory by proving the fact that perceived vulnerability, self-ecacy and response cost all played an important
role in determining users’ security behaviors.
The Theory of Planned Behavior (TPB) proposed by Ajzen in 2011 [
6
] explains that individuals depend on
intention to behave in a certain way and their ability to control that intention. Yang et al
.
[
195
]added six external
variables to TPB to build a comprehensive new model and validated the model with data collected from 216
survey participants. The results echoed the core concept of TPB that attitude, subjective norm and PBC (perceived
behavioral control) are positively related to behavioral intention of the user. Guhr et al
.
[
72
] developed a research
model based on several theoretical models, including TPB, to measure the eect of privacy concerns on the smart
device usage by home users. The study (survey, n=256) applied the partial least squared structural equation
modelling (PLS-SEM) to identify four essential elements to represent privacy concerns, including secondary use
of personal information, perceived surveillance, perceived intrusion, and awareness of privacy practice. Ferraris
et al. [56] as suggested in 4.2.4, put-forward a holistic trust model to improve security at home.
Some other old and new conceptual frameworks include the Technology Threat Avoidance Theory (TTAT) for
testing the IT Threat avoidance [
108
], negative-perception modelling for identifying barriers to smart home usage
by the elderly user [
141
], the privacy calculus theory to measure the relationship between perceived privacy risk
and the willingness to share privacy information [
92
], Innovation Resistant theory (IRT) and Multidimensional
Development Theory (MDT) to examine privacy concerns by Frick et al
.
[
59
] and the new model Perceived
surveillance of conversation (PSoC) developed by Pal et al
.
[
143
] to determine the cause of privacy concern.
Some of the new frameworks include the Stimuli-organisms-responses (S-O-R) framework for measuring the
balancing role of negative emotions such as anger, anxiety between privacy concerns and behaviors [
144
],
vulnerability-privacy concern-resistance (VPR) framework for explaining how users’ resistance to the adoption
of new technology is aected by their privacy concerns and their perception of their vulnerabilities Lee [104].
Although old and new theoretical frameworks have been used/developed to explore home users’ security and
privacy attitudes, practices and concerns, studies on using such frameworks to analyze security and privacy
aspects on the use of smart devices such as smart speakers are largely missing from the papers we covered.
In addition, it seems that such frameworks have not been incorporated into relevant ontologies to support related
research in a more holistic manner.
ACM Comput. Surv.
24 • Nandita Panaik, Shujun Li, and Jason R.C. Nurse
5 DISCUSSIONS & RECOMMENDED RESEARCH DIRECTIONS
The results of our meta-review and SLR showed active and extensive research on dierent topics in the broad area
of user perspectives of security and privacy aspects of home networks. However, our work also revealed many
research gaps, indicating that more research is still required. In this section, we summarize our core ndings
around seven recommended future research directions. Note that some can be mapped to a single RQ of our SLR,
but others cross-cut several RQs.
5.1 Co-existence of multiple connected devices in a single home
As mentioned in the Introduction Section, the average number of connected devices in an average household in
most Western countries is over seven [
101
]. The existence of many households with multiple home devices calls
for more research on the role of co-existence of multiple devices in a single home. Such needs have been met
by some research [
34
,
166
], but with limited depth and breadth. We observed three main research gaps. First,
most research that has been conducted focused on standalone devices or a specic type of devices such as smart
speakers [
1
,
42
,
46
,
145
], smart phone [
40
], and activity sensors [
153
]. Although recent research has explored
multiple devices in a connected home, especially data ows in a connected home [
36
,
39
,
91
], more research
is still needed to investigate how dierent types of home devices interact with each other, e.g., a smart
doorbell with a smart alarm, how such interactions are perceived by the users and how they aect security and
privacy of the home network as a whole. Second, the increasing number of home devices in a single home will
unavoidably complicate conguration and management of such devices, including their security and
privacy settings. Third, a large number of studies have concentrated on specic types of home devices, leaving
some types of home devices under-studied, especially dierent types of smart appliance. However, the
use of smart appliance at homes has been steadily increasing [
102
,
169
], so more research on such devices is
much needed.
5.2 Multiple users in a single home
According to the PRB (Population Reference Bureau) [
148
], the average household size worldwide in 2020 was
4.0, suggesting that most home networks have multiple users. Considering frequent and occasional visitors (e.g.,
neighbors, relatives, friends, carers and nannies) to a household, the number of users can be even larger. There are
also more complicated scenarios where the concepts of “home” and “regular occupants” are not clearly dened.
For instance, some members of a household split their time between two or even more “homes” (e.g., university
students and boarding school pupils live on campus during term time and go back to their parents’ house during
term breaks), some people living in the same neighborhood may share a broadband router where the “home”
network covers multiple households (which we could call an “extended home network”), and students sharing
a multi-room house may see it as a “pseudo-home”. Note that there can also be a hierarchical or graph-based
structure among multiple home users, possibly device-dependent (e.g., a primary user of a home device is a
secondary user of another home device). Research on the privacy and security issues in a multi-user home is
steadily growing [
28
,
63
,
82
,
113
,
117
,
118
,
197
,
200
]. However, most studies in this area are focused on access
control issues and power-play relationships between primary and secondary home users, overlooking the issues
of an increasingly hybrid and extended home occupants [
201
]. Research on many aspects of multiple users
in a single home, e.g., insider attacks and home users’ perception, security and privacy aspects of an “extended
home network” and a “pseudo-home” network, is missing from the current research literature.
5.3 Multiple contexts and contextual factors/parameters
Our SLR results showed evidence of research mainly in location- [
120
,
129
] or device-based contexts [
122
,
173
].
Although home itself may be considered a specic context, home users actually use the home network and home
ACM Comput. Surv.
A Survey of User Perspectives on Security and Privacy in a Home Networking Environment • 25
devices in the home for many dierent purposes, leading to multiple dierent (sub-)contexts of home networking
where security and privacy aspects have to be studied dierently. For instance, when using traditional computing
devices (personal computers) and “smarter” home devices, the context of use is very dierent. Similarly, when
working from home, there is a mixture and overlap between the work and home contexts. Furthermore, when a
user brings home devices (e.g., mobile devices and wearables) outside of the home for controlling home devices
remotely, an “extended home” context is created. More generally, each unique home networking scenario and
each specic type of home device could dene a unique context, and the dierent subsets of home devices that
work together for a specic purpose also dene dierent contexts. In addition to contexts dened by dierent
usage scenarios and user intention/purposes, some contexts are more overarching and should be considered
part of other contexts, e.g., the legal context regarding data protection matters about home devices that collect
personal data. Context-aware security and privacy is a signicant research area and some past studies [
157
] have
shown that concepts such as contextual histories dealing with the present and past contexts of the user can be
used to enhance the competency and predict future contexts [
50
] to adept user behaviors. However, as our SLR
showed, research on dierent contexts and contextual factors/parameters is still relatively limited, and future
work should venture into less-studied contexts.
5.4 Data flows across multiple devices, multiple users and in multiple contexts
Given the existence of multiple devices, multiple users and multiple contexts in a typical home network, and the
complicated relationships between them, there can be complicated unidirectional and bidirectional data ows of
dierent kinds, e.g., device-to-device, user-to-user, device-to-user, device-to-Internet, and user-to-Internet (the
last two are about data ows between the home and the external world, mainly external online services and
cloud servers on the Internet). These data ows might also dier in dierent contexts. Therefore, understanding
such data ows is of particular importance for analyzing security and privacy issues and for developing more
eective solutions. Researchers have been working around this topic [
38
,
91
,
155
] with a good deal of work
focusing on device-to-Internet phenomena [
20
,
173
], but a more systematic endeavor is still lacking. Hence, more
comprehensive research is needed to consider the complexities of data ows inside a home and from/to the
external world. These might consider dierent types of data ows between dierent entities in dierent
contexts, how they lead to security and/or privacy threats and risks, how home users perceive such data ows,
how home users respond to any security or privacy concerns, and how technical or socio-technical solutions can
be developed based on knowledge of such data ows. In addition, more experimental work is required to test
“hidden” data ows that are not explicitly specied in user manuals of home devices and privacy policies of the
manufacturers.
5.5 Demographic factors
Not surprisingly, our SLR revealed that gender and age are the two mostly studied demographic factors in the
literature. Some researchers also looked at eects of other demographic factors such as ethnicity, knowledge, edu-
cation level, income level, and disability, though with a limited depth. Furthermore, as mentioned in Section 4.2.10,
39% of the studies were conducted in UK, USA and Western Europe, suggesting a need for diversication
of countries covered. Chen et al
.
[
44
] suggested that, “geographical perspectives” could be instrumental in
deciding human behaviors and hence it is important that further research should consider less-studied areas.
While some demographic factors have been less or not studied, even for more-studied factor such as gender, we
have observed conicting results, so more research is needed to consolidate our understanding.
ACM Comput. Surv.
26 • Nandita Panaik, Shujun Li, and Jason R.C. Nurse
5.6 Home users’ awareness, perceptions, aitudes, behaviors and practices
Although a lot of work has been done on these aspects, the research gaps we identied in the previous subsections
suggest that there are still several gaps to be lled by further research. Many factors, including household types,
types of home networks including those with multiple geo-locations [
201
], dierent user structures [
118
,
200
],
dierent usage contexts and scenarios, and dierent demographic factors, can aect home users’ awareness,
perceptions, attitudes, behaviors and practices. According to some past studies [
27
,
31
,
73
,
95
], behavioral practices
could be inuenced by the level of awareness. Although dierent awareness enhancement initiatives are steadily
increasing to help users understand the nuances of security and privacy at home, they are still not eective
for non-expert users. Therefore, more research is needed to look into better ways and methods of increasing
privacy and security awareness of the users. Furthermore, a number of studies [
87
,
179
] looked into issues
of device congurations by users. These could be further investigated to address dierent issues, including
weak authentication [
177
] and multi-user authorization (automatic conguration) [
26
]. Dierent ML algorithms
including supervised classiers [
22
,
134
] and unsupervised learning algorithms such as LDA [
22
] have been
used in past studies to both predict and analyze user behavior. However, further attention in this area is needed,
especially in the context of multi-user and multi-device home to automatically learn and predict multi-user
behavior and issues in a connected landscape. In addition to more studies on less-studied areas, some more
holistic approaches are clearly needed, e.g., new taxonomies and ontologies that can cover dierent types of
user perspectives and inuencing factors.
5.7 Theoretical and conceptual frameworks
Our SLR has shown that past studies have considered the use and development of theoretical and conceptual
frameworks, but mostly focusing on behavioral frameworks (e.g., PMT and TPB). On a more technical front,
many taxonomies and ontologies have also been proposed [
19
,
78
,
160
,
168
], but they mostly have a limited scope
and do not cover user perspectives suciently or not at all. More precisely, there is much less work developing
theoretical and conceptual frameworks connecting home computing, IoT and smart home, user perspectives,
security and privacy aspects, and other important factors. We argue that a more advanced ontology needs
developing to have a more holistic and comprehensive view of security and privacy of home networks,
covering a wide range of aspects including at least the following: traditional computing devices (including
personal computers, routers and switches), smarter devices (including mobile devices, wearables, and IoT devices),
physical and virtual network topologies, relevant software tools and online services (including rmware in
hardware devices, mobile apps, smart speaker skills, online management tools and services, etc.), household
and user structures, demographic factors, mappings between home devices to capabilities, dierent threats and
defense mechanisms, dierent aspects of user perspectives (including awareness, attitude, perception, purposes
and intention, behavior, activities, and practices). Developing such a comprehensive ontology is not trivial, and
should be based on existing taxonomies and ontologies covering dierent areas.
6 CONCLUSION
The purpose of this study is to conduct a systematic review of published papers on user perspectives of security
and privacy aspects of a home network environment. It is evident from the results that this is quite a popular
area of research and the number of studies, especially towards the later part of the last decade, has increased
signicantly in number and in depth. Despite many research papers published, the focus of most past studies is
on issues and concerns arising from using a specic type of smart devices (i.e., smart speakers), their security
and privacy practices and related decisions, and underlying factors such as user trust and access control issues.
Few studies explored the issues of multiple types of connected devices inside a home network (including smart
devices, traditional computing devices, multiple smart devices and gateway devices, etc.) or considered the
ACM Comput. Surv.
A Survey of User Perspectives on Security and Privacy in a Home Networking Environment • 27
uid boundaries of a digital home. Additionally, some researchers also discussed multi-user related security and
privacy concerns and behaviors. Research also highlighted the role of location- and device-specic contexts, and
demographic factors, such as age and gender, in shaping users’ security and privacy behaviors. Furthermore, the
study collated theoretical and conceptual frameworks explaining the reasoning behind such users’ behaviors and
actions.
Our work revealed a number of important research gaps and calls for more research in a range of key research
areas, particularly around more holistic approaches (such as more advanced conceptual frameworks, especially a
more comprehensive home networking and smart home ontology) considering multiple and inter-connected
heterogeneous home devices, co-existence of several types of home users and other stakeholders, various contexts,
data ows between dierent entities and in dierent contexts, and more demographic factors. In other words, we
call for more future research to study the multi-dimensional complexity around security and privacy aspects of
home networks and user perspectives, in order to make future home networks and smart homes more secure and
privacy-friendly and meet people’s needs better.
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