A Survey of Big Data Security Solutions in Healthcare: 14th International Conference, SecureComm 2018, Singapore, Singapore, August 8-10, 2018, Proceedings, Part II

Abstract

Today data is a strategic asset and organizational goal is to maximize the value of their information. The concept of big data is now treated from different points of view covering its implications in many fields remarkably including Healthcare. Healthcare data is progressively being digitized and the Healthcare era is expansively using new machineries. Thus the medical data is increasing day by day has reached a momentous size all over the world. Although this data is being addressed as the basic to offer treasured insights and sinking cost, the security and privacy issues are so irresistible that medical industry is not capable to take full benefit of it. Privacy of Healthcare is a significant feature overseen by medical acts thus, the data must be secured from dwindling into the wrong hands or from being hacked. Due to the growing threats of loss and outflows from personal data and augmented acceptance of cloud technologies it is important to secure current Healthcare big data domain. This paper aims to present the state-of-the-art security and privacy issues in big data as pragmatic to Healthcare industry and discuss some available data privacy, data security, users’ access control mechanisms and approaches.

Full text

A Survey of Big Data Security Solutions
in Healthcare
Musfira Siddique1, Muhammad Ayzed Mirza1, Mudassar Ahmad1(B
),
Junaid Chaudhry2, and Rafiqul Islam3
1Department of Computer Science, National Textile University, Faisalabad, Pakistan
musfirasiddique@ymail.com,ayzed@ntu.edu.pk ,mudassar.utm@gmail.com
2Cyber Security Faculty, Cyber Intelligence and Security Department College of
Security and Intelligence, Embry-Riddle Aeronautical University Prescott, Prescott,
AZ, USA
chaudhrj@erau.edu
3School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW
2795, Australia
mislam@csu.edu.au
Abstract. Today data is a strategic asset and organizational goal is
to maximize the value of their information. The concept of big data is
now treated from different points of view covering its implications in
many fields remarkably including Healthcare. Healthcare data is pro-
gressively being digitized and the Healthcare era is expansively using
new machineries. Thus the medical data is increasing day by day has
reachedamomentoussizeallovertheworld.Althoughthisdataisbeing
addressed as the basic to offer treasured insights and sinking cost, the
security and privacy issues are so irresistible that medical industry is not
capable to take full benefit of it. Privacy of Healthcare is a significant
feature overseen by medical acts thus, the data must be secured from
dwindling into the wrong hands or from being hacked. Due to the grow-
ing threats of loss and outflows from personal data and augmented accep-
tance of cloud technologies it is important to secure current Healthcare
big data domain. This paper aims to present the state-of-the-art security
and privacy issues in big data as pragmatic to Healthcare industry and
discuss some available data privacy, data security, users’ access control
mechanisms and approaches.
Keywords: Big data ·Cloud ·Healthcare ·Security ·Analytics
1 Introduction
Big data is data sets that are so voluminous and complex that cannot be ana-
lyzed with traditional computing techniques. Big data philosophy encompasses
unstructured, semi-structured and structured data, however the core concentra-
tion is on unstructured data [1]. Data that is unstructured or time sensitive
or simply very large cannot be handled by relational database engines. Quite
c
ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018
R. Beyah et al. (Eds.): SecureComm 2018, LNICST 255, pp. 391–406, 2018.
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392 M. Siddique et al.
simply, big data reflects the changing world we live in. The more the variations
are taken and documented as data as the more things alternate. Take Facebook
as an example, Facebook handles almost 40 billion photos from its user base it
also handles audio and video data, per second. There are many Twitter tweets
handled per second and also YouTube data is generating enormously. Other
examples are Cloud and web data, social media data, time and location data,
scientific instruments and sensors data. Data has grown speedily, as of 2012,
every day 2.5 exabytes (2.5 ×1018) of data are generated. By 2025, Interna-
tional Data Corporation (IDC) predicts there will be 163 zettabytes of data. Big
data is often characterized by its three V’s. The extreme volume of data, the
wide variety of data types and the velocity at which the data must be processed
[2]. And some even extend this to five Vs and currently its extended to ten Vs
shown in Fig. 1and defined as follows [3–8]:
1.1 V’s of Big Data
–Volume: Volume is the amount of data. While volume specifies more data,
the data is generated from web, sensors, social media etc. It refers to the
amount of data generated after every second and classify as records, tables
and files.
–Veloci t y: Velocity is the fast rate at which data is received and possibly
proceeded. It is the in and out flow of data, the data upload and download
time and data at motion. It includes Batch velocity, near time data, Real
time Data and Social media data.
–Variety: Variety is the many forms of data. Like Structured, Unstructured,
semi-structured and multi-structure data types. Data such as text, audio,
and video need supplementary processing to both originate denotation and
the subsidiary metadata.
–Variability: In bigdata’s context, variability refers to different things. One is
inconsistencies/outliers in data. It is also variable due to multitude dimensions
of variables resulting from various data sources and dissimilar data types. It
also refers to variable frequency of bigdata to be loaded into database.
–Veraci t y: Veracity refers to the confidence to trust in data. This one is
the unfortunate part of bigdata. As most of the other characteristics are in
increasing trend but it drops. It refers more to the provenance or reliability
of the data source, its context, and how meaningful it is to the analysis based
on it. It helps us to determine the risk associated with the analysis or decision
made based on a particular dataset.
–Vali d ity : Much like veracity, validity refers to how correct and valid/accurate
data is to be used for the task. Scientist’s approximately 60% of time is
consumed to refine the data for what they conduct analysis. The analytical
advantage from bigdata is only as good as its primary data. There is a need
to have a good data governance practices to ensure consistent data class and
metadata.
–Vulnerability: Everybody is cautious regarding data security. Bigdata has
also got new security concerns. If bigdata is breached it would be a colossal

A Survey of Big Data Security Solutions in Healthcare 393
data breach. E.g. AshleyMedison hack in 2015 and in May 2016, 167 million
LinkedIn profiles and 360 million user passwords of MySpace hack had been
reported in the past. So it is a question mark against bigdata vulnerability.
–Volatility: Data volatility belongs to the data life, how old data should
have to be kept before it is considered as irrelevant. Or can say the data-
age when data is not useful. Due to huge amount of data in bigdata data
volatility is considered as an important and considerable point. Otherwise
organizations don’t bother to keep data for the life time archives and in live
databases without hindering the performance. Rules for data availability, need
and clear relationship of data with business process are to be defined for rapid
and cost-effective retrieval of information. Bigdata magnifies the complexity
the storage cost and retrieval process that’s why data volatility is needed.
–Visualization: It includes the data visualization tools and techniques.
Developing a meaningful visualization from the huge multitude variables and
their complex relationships of big data. It’s not an easy task to visualize that
huge and complex data. The traditional ways of graphing and plotting are
not sufficient, so different ways of representing data is needed. It may include
data clustering, tree maps, sunbursts, parallel coordinates, circular network
diagrams, or cone trees.
–Valu e : The most important of all is value, is the data into money it denotes
the scientific value attributed to this data.
BIGDATA
VULNERABILITY
VOLATILITY
VISUALIZATION
VARIABILITY
VELOCITY
VALIDITY
VOLUME VARIETY
VALUE
VARACITY
Fig. 1. Vs of big data
The importance of big data doesn’t revolve around how much data you have,
however what you do with it. You can take data from any source and it to find
answers that enable cost reductions, time reductions, new product development
and optimized offerings, and smart decision making. Data in its raw form has no

394 M. Siddique et al.
value. Data needs to be processed in order to be of valuable. Processing infor-
mation like this illustrates why big data has become so important. Unstructured
data needs enormous storage and processing, the Internet is increasing the raw
data day by day that needs to be processed [9].
There are many major issues related to big data. Big data analysis is one
of them, which is the process to uncover hidden patterns and unknown corre-
lations for actual decision making and better strategic moves. Others are big
data Security issues, big data management, Data Visualization, Data integra-
tion, Transition of big data to the cloud, new ways and technologies to protect
big data, Mining Big data, Processing issues, Gaining maximum value from big
data and predictive analytics. Some hot topics related to big data are Real time
big data analytics, IOT and Big data, big data security, big data in health care
and many more [10,11].
Fig. 2. Big data in Healthcare [12]
1.2 Healthcare Big Data
Generally the Healthcare data includes the patient data and elementary infor-
mation, clinical data and doctors’ data. However now a days the Healthcare
era is expansively using new machineries such as apprehending devices, sen-
sors, electronic health records and mobile computing etc. Thus the medical data
is increasing day by day the volume of Healthcare data is rapidly increasing.
Healthcare data is generating from internal and external sources like biomet-
ric data, genetics, blood pressure, electronic medical records, remote sensors
data and social media data. All the electronic Healthcare data, patient medical
records, surgeries results, medical images data, sensors data and all other med-
ical related data is added into Healthcare database and increasing its size to
a great extent [13]. This Healthcare data requires better real time analysis for
meaningful information.

A Survey of Big Data Security Solutions in Healthcare 395
Big data analytics has many benefits in Healthcare as it embeds better
decision making and reduce cost, it gives benefits to doctors and Healthcare
providers, perceiving dispersal diseases earlier, fraud detection, and evidence
based medicine and many more. Figure 2reflects the bigdata involvement in
healtcare system [12].
Healthcare data includes many challenges like Comfort of understanding
unstructured data and its use, mining hypothetically valuable information from
data in order to minimize faults, scalability, Reduction of cost in scrutinising
genomics data and mixing this type of data with other information is of great
importance, by using many sensors recording patients’ interactive data is of great
complexity. Above all the vital challenge in perspective of Healthcare domain is
the Security issue in Healthcare systems.
Security of the Healthcare Information System is the key concern from the
day one. Individual’s information must be protected from loss and from hack-
ers attack by using different physical security mechanism and techniques like
encryption, authentication, cryptographic algorithms etc. The main root of the
security issue is the use of Cloud in Healthcare systems as all the data storage
is on cloud and it provides different storage and processing facilities. Hence we
should tackle the security problem of Healthcare systems.
2 State-of-the-Art
Nowadays the backbone to the current storage devices is big data and Cloud
Computing. As E-Health has grown rapidly and its databases contains volu-
minous data. The security and privacy preservation is the key concern in this
respect. Cloud computing security is very important. The quality of Healthcare
should be increased by its security. They proposed a secure e-health framework
using hadoop Map reduce data that provides security for Healthcare. Proposed
a new framework, Multi Authority Attribute based Encryption (MA-ABE) for
securely transferred the PHI to health services after security checks. The encryp-
tion technique, Cipher Policy Attribute Based (CP-ABE) was used in it. The
research concentrations are with safeguarding health care from outbreaks by ille-
gal users and also detects the intimidations in health care. Accuracy, efficiency
and consistency are the performance parameters in the proposed solution. To
store the patient medical information securely any organization can use this
application as it used the best encryption technique. Also the efficiency of this
system is saleable as compared to existing system [14].
The modern Health Information Technology (HIT) electronically preserve
and transfer data globally in seconds and offers quality of service to Healthcare.
Thus by given the Electronic Medical Records (EMR) to every service provider
the main problem with the modern EMRs is that they are potentially central-
ized. Patients’ health information reclamation is a challenge. The aptitude to
generally access all patient Healthcare information in an appropriate fashion is
of highest importance. Health information must be available and obtain- able to

396 M. Siddique et al.
Security
Model
Big D ata
Analycs
Labs Radiology
Mobile Users
Different CDOs across the country
HL7 HL7
EMRPHR PHR
The Cloud
Different CDOs across the country
InsuranceBilli ngRegistraonPharmacy
EHR
HL7 HL7
Fig. 3. A framework for secure Healthcare system [15]
everyone tangled in the system. Thus, in order to deliver high-quality Health-
care to the patients they serve a high level of data integration, and sharing
among different Healthcare specialists and organizations is essential. Among dif-
ferent Healthcare providers, practitioners and patients the proposed framework
offers high level of integration, availability and sharing of data between them.
The detailed framework is shown in Fig.3. Mobile Cloud permits rapid Internet
access and endowment of EHRs from everyplace and at any time by altered plat-
forms. The proposed framework employs big data analytics to find useful insights
that help specialists proceeds acute decisions in the right time. It applies a set of
security constraints and access control that guarantee integrity, confidentiality,
and privacy of medical information [15].
Gunamalai et al. proposed a method of security and privacy of Personal
Medical Records and Digital Imaging and Communication in Medicine (DICOM)
images in the Cloud environment. DICOM discourses dissemination and inspect-
ing medical images and also is a typical rule for medical imaging. The penalty
area is to enable numerous medical centers to admittance individuals’ data for
treatment in a protected method. The structure untraceably implants remote
patient data like name and exclusive ID in the medical images. Access Governor

A Survey of Big Data Security Solutions in Healthcare 397
is done via two-way authentication. In Two way authentication firstly the user
log on by entering correct id and password and secondly the user has to enter
the key sent to him after login on his mobile with in a given time period. Thus in
this way the user will be verified and get access to cloud data storage. By using
Column based encryption Healthcare centres can encrypt their PMR and images
to allow granule access. By using column based encryption the user who know
the encrypted password can access the specific columns’ data. Column Based
Encryption (CBE) permits discerning division of data amongst medical centers
[16].
Security of patient data is vital in cloud based big data cooperative structure
where they offer a platform for sharing and exchanging information exist in
different clouds for numerous errands. There is a proposed scheme which is a
two phase security protocol that uses pairing based cryptography. Secret data
is shared by computing a secret session key which is dynamically generated for
every new data-exchange session by computing a pairing in elliptic curve. Hence
each new session is no dependent on the previous one. It also gives security
against man-in-the-middle attack. Response time, memory and availability are
the performance parameters in the proposed solution [17].
Medical content security need is increasing by the extended use of Healthcare
management systems. The authors proposed an authentication based access con-
trol mechanism for medical content DICOM. The confidentiality of the DICOM
in public cloud is ensured by the Access control mechanism. It also provides the
integrity of the user detail in Healthcare system [18].
As the EMR of patient need to be accessed by the Healthcare experts. For
the ease of access the EMRs need to be stored at Healthcare cloud in big data
storages. However the key concern with the Healthcare cloud is its security as
the patients sensitive information needs to be secure because data stealing out-
breaks are well-thought-out to be one of the vital security breaks Clouds Health-
care data. By using a decoy technique with fog computing facility they present a
methodology to prevent patient MBD in Healthcare cloud. Decoy files retrieved
at the start thus, as to make system secure by hiding the original file. It uses
a double security technique by the encryption of genuine file to prevent system
from attackers. Key generation time, accuracy and availability are the perfor-
mance parameters in the proposed solution. As a result, the proposed method-
ology guarantees that the MBD of users are 100% protected and reduces the
process [19].
Big data contains voluminous amount of data which is growing rapidly day
by day. However the booming of big data also hinges on fully accepting and
handling newly rising safety and confidentiality trials. The security and privacy
preservation of big data is the key concern. New extracted information will be
unpersuasive if data is not reliable, while if confidentiality is not well addressed,
people may be disinclined to share their data. This have introduced an efficient
and privacy-preserving cosine similarity computing protocol in response to the
efficiency and privacy requirements of data mining in the big data era. Although
have analyzed the privacy and efficiency challenges in general big data analytics

398 M. Siddique et al.
to shed light on the privacy research in big data, significant research efforts
should be further put into addressing unique privacy issues in some specific
big data analytics. Encrypt/decrypt time, speed and de-identification are the
performance parameters in the proposed solution [20,21].
The modern Health Information Technology (HIT) electronically preserve
and transfer data globally in seconds and offers quality of service to Healthcare.
Thus by given the EMR to every service provider the main problem with the
modern EMRs is that they are potentially centralized. Patients’ health infor-
mation reclamation is a challenge. The aptitude to generally access all patient
Healthcare information in an appropriate fashion is of highest importance. The
first contribution of this research is the provision of an overall picture on big
data and Healthcare data for non-expert readers. The other one is the adoption
of a holistic view to build an organized Healthcare model for protecting patient
data. The model provides high-level integration and sharing of EHRs. The sug-
gested framework as shown in Fig.4applies a set of security constraints and
access control that guarantee integrity, confidentiality and privacy for medical
data [22].
Security
Model
Big Data
Analycs
CDO
PHR
The Cloud
CDO
End Us er
CDO
End Us er
End Us er
CDO
End Us er
CDO
EHR
End Us er
CDO
End Us er
Fig. 4. A framework for distributed Health system [22]
Security is a key concern in Remote Patient Monitoring framework as there
is a little evidence on this problem’s solution. The authors proposed a new secu-
rity framework for Patient Remote Monitoring devices which is a wide-ranging
model. They projected NFC technology to tackle the problem of multi user device
patient identification. Firstly a patient identifies using NFC no and take their

A Survey of Big Data Security Solutions in Healthcare 399
Table 1. Comparison of different security mechanism
Sr. No Author Problem tackled Security mechanism Performance parameters
1B. Prasanna
et al.
Big data and Cloud
security
CP-ABE scheme Response time,
accuracy
2 Ahmed
E. Youssef
Mobile cloud
security,
AES and RC4
encryption
Key generation
time, response time,
encrypt/decrypt
time
3 Gunamalai
et al.
Information security Two way
authentication and
CBE
Tate pairing time,
memory
4Mehedi et al. Big data and Cloud Pairing based
cryptography
Confidentiality,
integrity
5Subhasri
et al.
Security Authentication based
access control
mechanism
Response time,
memory
requirement,
accuracy
6Hadeal Abdul
aziz et al.
Big data security in Fog computing with
pairing based
cryptography
Key generation
time, integrity,
speed
7Sathya et al. Collaborative
environment
Triple DES Integrity,
confidentiality,
availability
8Bikash Kanti
Sarkar
DICOM content AES and RC4
encryption
Authentication,
availability
9Brian
Ondiege et al.
Security in cloud NFC for
identification
Authentication,
availability
10 Chao YANG
et al.
Big data and Cloud triple encryption
method
Encrypt/decrypt
time, memory
11 Rui Zhang,
Ling Liu
Big data Cloud
security
Attribute-based
Composite,
encryption
Response time,
memory
requirement,
accuracy
12 Ali Gholami
and Erwin
Laure
Cloud security NFC for
identification, DES
etc
Integrity,
confidentiality,
availability
13 B. Vinoth
Kumar et al.
Patient monitoring
security (IOT and
Cloud)
AES and DES
encryption
Key generation
time, memory used,
availability
14 Weiwei LIN
et al.
Data security
WBANs
Multi biometric
based key generation
scheme
Confidentiality,
integrity
15 Farrukh
Aslam Khan
et al.
Remote patient
monitoring security
NFC for
identification
Integrity,
confidentiality,
availability
B.P reading. Then system checks if that patient exists and able to send its read-
ing, thus it will continue its procedure. The also enable capability system which
allows only registered devices to send their readings via secure communication
protocol. They uses the performance parameters like accuracy, availability and
memory used [23].

400 M. Siddique et al.
As Healthcare data is increasing rapidly and it need better storage, for this
purpose cloud is used. However the security of the data in cloud is a threat. To
address this problem the authors proposed a Novel Triple Encryption method.
In the triple encryption scheme, HDFS files are encrypted by using the hybrid
encryption based on DES and RSA, and the user’s RSA private key is encrypted
using IDEA. The triple encryption scheme is implemented and integrated in
Hadoop-based cloud data storage. Encrypt/decrypt time, memory and availabil-
ity are the performance parameters in the proposed solution. Results of experi-
ment show that the triple encryption scheme is feasible, it meets the reading and
writing characteristics of HDFS and can enhance the confidentiality of default
HDFS [24]. Table 1shows a summarized comparison of different security algo-
rithms proposed by different researchers.
3 Big Data Security Solutions in Healthcare
The main security models discussed in this paper are related to big data Health-
care and cloud, big data and IOT in Healthcare and securing patient data in
cloud and big data architectures.
3.1 Cloud and Big Data in Healthcare
Cloud computing is a shared pool of configurable computing resources. Now a
days for handling of Healthcare data and Healthcare information classifications
cloud computing in comprehensively used [13]. As Healthcare organizations has
been moved to electronic platform today from where it gathers amply of data.
The significant amount of data need to be stored and processed. Cloud comput-
ing is best suited for Healthcare domain. It provides many benefits as it makes
data sharing easy and more handy for the user, it provides cost reduction opera-
tions. However with the electronic medical records there is a chance of data loss
and other information loss. Cloud computing sideways with Big Data tools has
Initiate use in Curative Imaging, clinic organization and Healthcare Information
Systems, public health and individual’s self-service applications. With the ris-
ing use of cloud computing tools in Healthcare it is authoritative that, security
of Healthcare data in the cloud is a key concern that needs to be well-kept-up
sideways with secure the cloud computing.
Fog Computing Facility with Pairing-Based Cryptography
Fog computing, is an evolving model that offers storing, dispensation, and com-
munication amenities nearer to the end user. Providing data and tapping them
on the upper hand of a network to be closer to the user are well-thought-out
amongst the main tasks of fog computing. The proposed method as shown in
Fig. 5provide the user’s multimedia data security by using fog computing. A
well-organized tri-party genuine key covenant protocol has been proposed based
on paring cryptography among the user, the DPG, and the OPG. This is an
illusion technique as it provides the attacker a decoy gallery rather than the
original one. As shown in figure when the user log on whether it is an attacker

A Survey of Big Data Security Solutions in Healthcare 401
Fig. 5. Proposed security framework using fog computing facility [19]
or legitimate user it will be shown the DMBD which is a decoy gallery. Then
the second step will be the verification of the legitimate user as he knows that
DMBD is fake thus, he will access original OMBD by verifying himself. Thus
in this way the original MBD will remain safe from hackers. The algorithms
used are DMBD algorithm, Key exchange algorithm, user profiling algorithm
and photo encryption and decryption algorithm [19].
Two Way Authentication and Column Based Encryption
Authentication is a process of identifying the user who has stances the rights to
access and modify data on cloud. In Two way authentication firstly the user log
on by entering correct id and password and secondly the user has to enter the
key sent to him after login on his mobile with in a given time period. Thus in
this way the user will be verified and get access to cloud data storage. By using
Column based encryption Healthcare centers can encrypt their PMR and images
to allow granule access. By using column based encryption the user who know
the encrypted password can access the specific columns’ data. Hence it allows
many Healthcare centers to access patients’ data for treatment in a secure way.
Figure 6shows a scenario in which multiple centers can access multiple columns
by using the secret key [16].

402 M. Siddique et al.
Column 1
(Paent’s Detail)
Colu mn 2
(Test Report)
Colu mn 3
(Medical Images)
Colu mn 4
(Hospital Bills)
G15qnhvcb6
AxhyMiwc
Fihskhvc Bx6x+xtcMbC
Bxc6XHykbcc
byqxkitB
Sa8wasf5ntb
S4/aSfgasfsns
S8gs5wasf5nt
W3nus81e
R4SsfaC
T8nus4bc
User 1 User 2 User 3 User 4
Key 1 Key 2 Key 3 Key 4 Key n
------------Personal User (PEU)------------ --------Public User (PBU)--------
. . . . . . . .
User N
. . .
Doctor or
Consultant
Accounts
Manager
Insurance
Comp any
Other Healthcare
Cent re
Fig. 6. Proposed security framework using column based encryption [16]
3.2 IOT and Big Data in Healthcare
To monitor the medical disorder of patient Wireless Body Area Networks or
WBANs are used by using miniature sensor bulges entrenched to the human
body. Wireless Sensor Network (WSN) is developed by these sensor bulges. From
the human body the biological information is sent to a regulator device either
devoted to the human body or in the 90 locality wirelessly. Then for more analysis
the gathered data is sent to isolated servers or cloud of a hospital/Healthcare
center. WBANs can be used for blood flow, ECG, pulse rate, blood pressure,
body temperature etc. It is vigorous to guarantee precision and veracity of such
Healthcare data [29–31]. Hereafter security and privacy of WBANs must be
safeguarded. Security must be preserved for WBANs in the attached sensors to
the body, isolated servers where WBAN data is pushed, communication medium.
Multi Biometric Based Key Generation Process for Securing WBANs
In patient monitoring the patients’ data is gathered though the sensors attached
to the patients’ body and sent to the remote servers for further actions. Multi
biometric scheme is used for securely generate the key. It is useful for secure
inter sensor communication. Features are selected from ECG and EEG values
and quantized hence divide in blocks and exchanged by applying key hashing. At

A Survey of Big Data Security Solutions in Healthcare 403
the receiving end the key generation algorithm is applied for extraction of infor-
mation and then both the sensor nodes use this key for secure communication
[23]. The flow of security framework is shown in Fig. 7. In patient monitoring the
patients’ data is gathered though the sensors attached to the patients’ body and
sent to the remote servers for further actions. Multi biometric scheme is used
for securely generate the key. It is useful for secure inter sensor communication.
Features are selected from ECG and EEG values and quantized hence divide
in blocks and exchanged by applying key hashing. At the receiving end the key
generation algorithm is applied for extraction of information and then both the
sensor nodes use this key for secure communication. Then for more analysis the
gathered data is sent to isolated servers or cloud of a hospital/Healthcare center.
WBANs can be used for blood flow, ECG, pulse rate, blood pressure, body tem-
perature etc. It is vigorous to guarantee precision and veracity of such Healthcare
data. Hereafter security and privacy of WBANs must be safeguarded. Security
must be preserved for WBANs in the attached sensors to the body, isolated
servers where WBAN data medium.
Feature
Generaon
ECG Si gnal
EEG Signal
Quanzaon Block
Exchange
Key ECG
Key EEG
ECG FV
EEG FV
ECG Bl ocks
EEG Block s
Keygen
Keygen
Final Key
Horizo ntal C oncatena Ɵon
Fig. 7. Proposed security framework using multi biometric based key generation [23]
4 Discussion
In this paper, i have examined the security and privacy challenges in big data, by
deliberating some existing mechanisms and techniques for achieving security and
privacy in which Healthcare administrations are likely to be highly beneficial.
The security challenges mainly include the security of patient data on cloud, the
secure sharing of data in cloud collaborative environments, protect the patient
medical records, sensors data security in remote patient monitoring systems, and
information security. Many techniques were used to tackle these challenges like
triple encryption, pairing based cryptography, CP-ABS mechanism, fog comput-
ing, two way authentication and Column based encryption and authentication
based access control mechanism. From all of these Fog computing Technique
with Pairing based Cryptography, two way authentication and CBE have shown
better results. Future work may include to take one of these mechanism and
apply it on some dataset to improve the security on cloud.

404 M. Siddique et al.
5 Conclusion
While Big Data technologies are improving day by day this also means that
the volume of data along with the rate at which data is flowing into enterprises
today is increasing. Healthcare data is progressively being digitized now a days
the Healthcare era is expansively using new machineries such as apprehending
devices, sensors, electronic health records and mobile computing etc. Thus the
medical data is increasing day by day has reached a momentous size all over
the world. Although this data is being addressed as the basic to offer treasured
insights and sinking cost, the security and privacy issues are so irresistible that
Medical industry is not capable to take full benefit of it. Privacy of Healthcare
is a significant feature overseen by Medical Acts thus, the data must be secured
from dwindling into the wrong hands or from being hacked. Due to the growing
threats of loss and outflows from personal data and augmented acceptance of
cloud technologies it is important to secure current Healthcare big data domain.
This paper aims to present the state of- the-art security and privacy issues in
big data as pragmatic to Healthcare industry and discuss some available data
privacy, data security, users’ access control mechanisms and approaches.
References
1. Martınez Sesmero, J.M.: Big data application and utility for the healthcare system.
Farm Hos p 39(2), 69–70 (2015)
2. Shin, D., Sahama, T., Gajanayake, R.: Secured e-health data retrieval in DaaS
and big data. In: Proceedings of the 2013 IEEE 15th International Conference on
e-Health Networking, Applications and Services, (Healthcom 2013), pp. 255–259.
IEEE, Lisbon, Portugal, October 2013
3. Chang, V.A.: Amodel to compare cloud and non-cloud storage of big data. Future
Gener. Comput. Syst. 57, 56–76 (2016)
4. Huang, T., Lan, L., Fang, X., An, P., Min, J., Wang, F.: Promises and challenges
of big data computing in health sciences. Big Data Res. 2(1), 2–11 (2015)
5. Firican, G.:The 10 Vs of Big Data, TDWI, 8 February 2017. https://tdwi.org/
articles/2017/02/08/10-vs-of-big-data.aspx. Accessed 23 Apr 2018
6. Chen, C.L.P., Zhang, C.Y.: Data-intensive applications, challenges, techniques and
technologies: a survey on big data. Inf. Sci. 275, 314–347 (2014)
7. Logica, B., Magdalena, R.: Using big data in the academic environment. Procedia
Econ. Financ. 33, 277–286 (2015)
8. Agrawal, D., El Abbadi, A., Arora, V.,et al.: Mind your Ps and Vs: a perspective
on the challenges of big data management 6 wireless communications and mobile
computing and privacy concerns. In: Proceedings of the 2015 International Con-
ference on Big Data and Smart Computing, (BIGCOMP 2015), pp. 1–6, Republic
of Korea, February 2015
9. Sabar, N.R., Abawajy, J., Yearwood, J.: Heterogeneous cooperative co-evolution
memetic differential evolution algorithm for big data optimization problems. IEEE
Trans. Evol. Comput. 21(2), 315–327
10. Jina, X., Waha, B., Chenga, X., Wanga, Y.: Significance and challenges of big data
research. Big Data Res. 2, 59–64 (2015)

A Survey of Big Data Security Solutions in Healthcare 405
11. Mirza, M.A., Habib, M.A.: Optimized energy ingestion in IoT enabled sensor nodes:
a survey. J. Softw. Eng. Intell. Syst. 2(3), 3 (2017). E-ISSN: 2518–8739
12. Big data in HealthCare. https://www.google.com/imgres?imgurl=http
13. Widmer, A., Schaer, R., Markonis, D., M¨uller, H.: Gesture interaction for content-
based medical image retrieval. In: Proceedings of the 4th ACM International Con-
ference on Multimedia Retrieval, pp. 503–506. ACM, New York (2014)
14. Jina, X., Waha, B., Chenga, X., Wanga, Y.: E-health for security and privacy in
health caresystem using hadoop map reduce. Big Data Res. 2, 59–64 (2015)
15. Youssef, A.E.: A framework for secure healthcare systems based on big data analyt-
ics in mobile cloud computing environments. Int. J. Ambient Syst. Appl. (IJASA)
2(2) (2014)
16. Gunamalai, C., Sivasubramanian, S.: Novel method of security and privacy for
personal medical record and DICOM images in cloud computing. ARPN J. Eng.
Appl. Sci. 2, 59–64 (2015)
17. Masud, M., Hossain, M.S.: Secure data-exchange protocol in a cloud-based collab-
orative health care environment. Big Data Res. 2, 59–64 (2017)
18. Subhasri, P., Padmapriya, A.: Authentication based access control mechanism for
ensuring privacy of DICOM contents in public cloud. Aust. J. Basic Appl. Sci.
11(10), 128–136 (2017)
19. Al Hamid, H.A., Mizanur, Sk. Md.: A security model for preserving the privacy of
medical big data in a healthcare cloud using a fog computing facility with pairing-
based cryptography. IEEE Access 2, 59-64 (2016)
20. Sathya, S., Sethukarasi, T.: Efficient privacy preservation technique for healthcare
records using big data. In: International Conference On Information Communica-
tion And Embedded System (ICICES 2016) (2016)
21. Victor, N., Lopez, D., Abawajy, J.H.: Privacy models for big data: a survey. Int.
J.BigDataIntell.3(1), 61–75
22. Sarkar, B.K.: Big data for secure healthcare system: a conceptual design. Big Data
Croos Mark 2, 59–64 (2017)
23. Ondiege, B., Clarke, M., Mapp, G.: Exploring a new security framework for remote
patient monitoring devices. Big Data Res. 2, 59–64 (2017)
24. Yang, C., Lin, W., Liu, M.: A novel triple encryption scheme for hadoop-based
cloud data security. In: 2014 Fourth International Conference on Emerging Intelli-
gent Data and Web Technologies (2014)
25. Zhang, R., Liu, L.: Security models and requirements for healthcare application
clouds. In: 2017 Fourth International Conference on Emerging Intelligent Data and
Web Technologies (2017)
26. Gholami, A., Laure, E.: Security and privacy of sensitive data in cloud computing:
a survey of recent developments, vol. 2. Springer (2016)
27. Vinoth Kumar, B., Ramaswami, M., Swathika, P.: Data security on patient moni-
toring for future healthcare application. Int. J. Comput. Appl. 163(6), 0975–8887
(2017)
28. Khan, F.A., Alia, A., et al.: A cloud-based healthcare framework for security and
patients’ data privacy using wireless body area networks. In: The 2nd International
Workshop on Communications and Sensor Networks (ComSense-2014) (2014)
29. Chaudhry, J.A., Tariq, U., Amin, M.A., Rittenhouse, R.G.: Sinkhole vulnerabilities
in wireless sensor networks. Int. J. Secur. Appl. 8(1), 401–410 (2014)
30. Rittenhouse, R.G., Chaudry, J.A., Lee, M.: Security in graphical authentication.
Int. J. Secur. Appl. 7(3), 347–356 (2013)

406 M. Siddique et al.
31. Jabbar, S., Ahmad, M., Malik, K.R., Khalid, S., Chaudhry, J., Aldabbas, O.:
Designing an energy-aware mechanism for lifetime improvement of wireless sen-
sor networks: a comprehensive study. Mobile Netw. Appl. 23, 1–14 (2018)
32. Malik, K.R., Farhan, M., Habib, M.A., Khalid, S., Ahmad, M., Ghafir, I.: Remote
access capability embedded in linked data using bi-directional transformation:
issues and simulation. Sustain. Cities Soc. 38, 662–674 (2018)