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Journal of Experimental & Theoretical Artificial
Intelligence
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/teta20
Blockchain-based privacy preservation framework
for healthcare data in cloud environment
Garima Verma
To cite this article: Garima Verma (2022): Blockchain-based privacy preservation framework for
healthcare data in cloud environment, Journal of Experimental & Theoretical Artificial Intelligence,
DOI: 10.1080/0952813X.2022.2135611
To link to this article: https://doi.org/10.1080/0952813X.2022.2135611
Published online: 21 Nov 2022.
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ARTICLE
Blockchain-based privacy preservation framework for healthcare
data in cloud environment
Garima Verma
School of Computing, DIT University, Dehradun, India
ABSTRACT
The storage of Electronic Health Records (EHRs) on mobile cloud environ-
ments has undergone a paradigm shift in recent years, with mobile
devices integrating with cloud computing to improve medical data trans-
fers between patients and healthcare providers. This sophisticated para-
digm allows for minimal operational costs, signicant exibility, and the
use of electronic health records (EHRs). However, for e-health systems, this
new paradigm poses concerns regarding data privacy and network secur-
ity. It’s a challenging problem to exchange EHRs consistently among
mobile users while maintaining high-security levels in the mobile cloud.
It’s a challenging problem to exchange EHRs consistently among mobile
users while maintaining high-security levels in the mobile cloud. Here, this
paper intends to introduce a novel blockchain technology for secure
health data in the cloud, which aids in ensuring authentication and oers
integrity to medical records. Here, blockchain with optimal encryption is
deployed via an improved blowsh model that also guarantees authenti-
cation features. Further, the optimal key generation is carried out by a new
approach termed as Elephant Herding Optimization with Opposition-
based Learning (EHO-OBL). Thus, the data integrity is maintained by the
developed approach, and at last, the supremacy of the presented
approach is proved concerning various measures. Accordingly, the key
generation time of the proposed method has attained less value, and it
was 51.04%, 91.48%, 92.64%, 91.48%, 89.99%, 91.06% and 91.48% better
than traditional Blowsh, Rivest–Shamir–Adleman (RSA), Advanced
Encryption Standard (AES), Elliptic-Curve Cryptography (ECC), Elephant
Herding Optimization (EHO), Moth-Flame Optimization (MFO) and Whale
Optimization (WOA) models, for le size of 10 kb.
ARTICLE HISTORY
Received 6 May 2021
Accepted 9 October 2022
KEYWORDS
Security; privacy
preservation; blockchain;
data integrity; EHO-OBL
approach
Introduction
Recently, there has been a rising awareness in deploying blockchain technology to promote e-health
and medical services (Roehrs et al., 2019; Tripathi et al., 2019). Blockchain, with its trustworthy and
decentralised nature, has revealed huge potentials in a variety of e-health areas like data access
management and secured sharing of EHRs amongst numerous medicinal entities. Consequently, the
implementation of blockchain can oer capable solutions to assist medical deliverance and thus
transform the healthcare industry (Armoogum & Khonje, 2021; Mubarakali et al., 2020; Mubarakali,
2020; Zhang et al., 2021).
With the appearance of novel technologies, together with the internet Of Medical Things (IoMT)
and Mobile Cloud Computing (MCC), the medical industry has undergone remarkable
CONTACT Garima Verma garimaverma.research@gmail.com School of Computing, DIT University, Vedanta Building, 3rd
Floor, Dehradun 248009, India
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE
https://doi.org/10.1080/0952813X.2022.2135611
© 2022 Informa UK Limited, trading as Taylor & Francis Group
transformations in e-health functions (Ari et al., 2020; Xavier et al., 2020). Patients now can gather
their individual health details at their residence depending on cellular devices (such as wearable
sensors and smartphones) that can be shared on thecloud environment, wherein, the healthcare
provider can instantly access and examine medical records and oer appropriate health check
supports (Celesti et al., 2019; Fortino et al., 2018; Tian et al., 2019a). This smarter e-health service
permits medical providers to scrutinise patients remotely and proer ambulatory concerns at home
that enables medical delivery and oers economic advantages to patients. In addition, the accessi-
bility of entire EHRs on clouds also aids healthcare contributors in tracking patient healthiness and
provides appropriate medicinal services throughout treatment and diagnosis processes (Gumaei
et al., 2019; Sun, 2020; Xu et al., 2018).
Apart from all these greater benets, the tendency of EHRs storage also causes security confronts in
clouds that obstruct the employment of health appliances on the cloud (Azeez & Van der Vyver, 2019;
Hassan et al., 2019; Tian et al., 2019b). Therefore, EHRs need to carry out a secure sharing amongst
medical health providers and patients on a mobile cloud environment. Illegal entities might achieve
malevolent access to EHRs with no approval from patients that includes disadvantageous eects on
security, privacy and data condentiality of e-health cloud systems (Feng et al., 2019; Yang et al., 2019).
Furthermore, patients might nd it hard to manage and track their medical records distributed
amongst healthcare providers on cloud. Thus, it is essential to recommend a professional access
control solution for the cloud EHR sharing system (Chen et al., 2019; Wang et al., 2019).
The contribution of the work is given below:
●As a novelty, this paper introduces blockchain technology with optimal encryption for securing
medical data in the cloud.
●Establishes a new, improved blowsh algorithm for carrying out data encryption.
●Proposing a novel algorithm known as Elephant Herding Optimization with Opposition-based
Learning for optimal key generation.
The paper is arranged as: section 2 reviews traditional privacy preservation in healthcare frame-
works. Section 3 portrays the developed privacy preservation model in the cloud for health care
systems. Further, section 4 addresses the proposed EHO-OBL algorithm for optimal key generation.
Finally, the results and conclusion are elucidated in section5 and 6 correspondingly.
Literature review
Related works
In Mubarakali et al. (2019) have proposed a Secure and Ecient Health Record Transaction Utilizing
Block-chain (SEHRTB) model that addressed EHR information operation among the institutions,
patients, service providers, and doctors in a privacy-conserving manner. Here, the work provided
a medical sector with a block-chain technique. As a result, in the medical eld, the patient was
enabled to control and distribute their health records in cloud space in a secure way without any
destruction of condentiality. Moreover, it provided an eective method that ensured the patient’s
data condentially in intellectual health systems. Finally, the simulated experimentations have
revealed that the presented method oered enhanced performance in terms of latency and
throughput.
In Al Omar et al. (2019) presented a patient-based system for data management through
a blockchain model that helped to accomplish privacy. Accordingly, Cryptographic tasks were
utilised that encrypted patient data and guaranteed pseudonymity. Finally, by carrying out
a comprehensive analysis, the performance of the developed technique was veried in terms of cost-
eectiveness.
2G. VERMA
In Cao et al. (2019) presented a secure e-Health system in the cloud to guard the EHRs against
illegitimate alteration via the blockchain approach. The major plan was that the EHRs could be
outsourced only by authenticated contestants and every process on EHRs was incorporated into the
public blockchain as an operation. Accordingly, the EHRs could not be customised after related
transactions were recorded in the blockchain. At last, security examination and performance assess-
ment demonstrated a stronger security assurance with higher eectiveness for the presented
system.
In Nguyen et al. (2019) have proposed an EHRs sharing approach, which combined block chain
and ‘decentralised Interplanetary File System (IPFS) on a mobile cloud platform’. Predominantly,
a reliable access control method was designed with smarter contracts to secure EHRs sharing
amongst medicinal providers and diverse patients. Further, a prototype execution was presented
via ‘Ethereum block-chain’ on a cellular app. The experiential results have shown that the presented
model oers an eectual resolution for consistent data exchange on clouds against probable threats.
In Huang et al. (2020) have proposed a blockchain-oriented privacy-conserving method that
realised secured sharing of medicinal data among numerous entities, concerned patients, semi-
trusted cloud servers and research institutions. In the meantime, it achieved the data consistency
and availability amid research institutions and patients, whereby zero-knowledge evidence was
deployed to conrm whether the patient’s medicinal data meet the denite needs without exposing
patients’ condentiality. Then, the proxy re-encryption tool was used that guaranteed the decryption
of ciphertext.
In Yue et al. (2016) have adopted Healthcare Data Gateway (HGD) App that depends on block-
chain to facilitate patients to share, control and own their own data securely and easily devoid of
breaking privacy that provided a new probable means to develop the intellect of healthcare systems
while maintaining patient data condential. The adopted purpose-centric access scheme guaran-
teed the privacy of healthcare data. Moreover, integrated Indicator-Centric Schema (ICS) made it
feasible to systematise all types of individual healthcare data easily and practically.
In Dwivedi et al. (2019) have tried to sort out the problems of medical data privacy by means of
blockchain with the Internet of Things (IoT). A new customised blockchain framework was proposed,
appropriate for IoT devices that rely on additional privacy and network security properties. The
adopted model’s supplementary security and privacy properties depended on sophisticated crypto-
graphic primitives. The solution specied in this work makes IoT appliance transactions more
anonymous and secured via block chain-model.
In Kuo et al. (2020) have proposed a framework that combined ‘level-wise model learning,
blockchain-based model dissemination, and a novel hierarchical consensus algorithm for model
ensemble’. In addition, an ‘example implementation Hierarchical Chain (hierarchical privacy-
preserving modelling on blockchain)’ was presented, execution time and learning iteration was
assessed with existing methods modelled for at network topologies.
Review
Table 1 explains the reviews on traditional privacy preservation methods via cloud in the healthcare
system. More research works are exploited regarding this concept and the methodologies related
with their works are explained with their pros and cons are explained as follows: SEHRTB (Mubarakali
et al., 2019) has reduced latency with increased throughput. Future work intends to estimate the
feasibility of the system. MediBchain protocol (Al Omar et al., 2019) improves the time consumption
and satises all requirements. However, need to explore interoperability between dierent entities of
healthcare processes. Tamper-Proong-Electronic Health Records (TP-EHR; Cao et al., 2019) is safe
against diverse existing attacks and poses practical and ecient communication and computation
overhead. Further, the investigation should consider the blockchain technology for eHealth systems.
IPFS (Nguyen et al., 2019) exploits the sharing of medical data reliably and quickly, yet further needs
to consider the professional managing of e-health records of cellular clouds in the future. Proxy re-
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 3
encryption-based model (Huang et al., 2020) ensures condentiality with minimal execution time;
however, there was no optimisation on implementation. HGD architecture is the used methods in
(Yue et al., 2016), in which the patients are aware of who is accessing their data and acquire simple
authoritarian decisions regarding storing, sharing and collecting patient data. However, other
optimization concepts are needed for the eective management of data. The secure hash
Algorithm adopted in (Dwivedi et al., 2019) is very secure and results in better privacy.
Nevertheless, it needs consideration on DDOS attacks. The consensus algorithm used in (Kuo et al.,
2020) oers minimal execution time with minimal overhead, but it needs to evaluate it in a real-
world environment.
Developed privacy preservation model for secured health care systems in thecloud
The developed Keyless Signature Infrastructure (KSIBC) model aids in securing the patient records
eciently. A doctor raises requests for health data by oering their own ID and patient’s private key
to treat a patient. The data recovered by the doctor are stored in the local database. In addition, by
deploying the oered ID, access control of the individual is conrmed in the Access Control List (ACL;
Nagasubramanian et al., 2020). If the users are authentic at the early stage, dierent processes of KSI
are executed for signing the data to guarantee digital integrity. This includes an initial gateway,
which holds the role of an initial aggregator to check. After that, the core aids in ensuring
condentiality and also guarantees data security in the developed approach. In addition, the data
are transmitted into the blockchain for processing it for further validation. The medical data of every
patient are accumulated as a block in the blockchain. By deploying blockchain techniques, data
security is guaranteed greater in the developed KSIBC model.
Table 1. Reviews on blockchain-based privacy preservation in the healthcare system.
Author
Implemented
methods Features Challenges
Mubarakali
et al.
(2019)
SEHRTB ●Increased throughput
●Reduced latency
●No estimation on the feasibility of the
system
Al Omar
et al.
(2019)
MediBchain
protocol
●Satisfies all requirements
●Improved time consumption
●Need to explore interoperability between
different entities of healthcare processes
Cao et al.
(2019)
TP-EHR ●Safe against diverse existing
attacks
●Pose practical and efficient com-
munication and computation
overhead
●Further investigation needed to utilize the
blockchain technology to enhance e-Health
systems
Nguyen
et al.
(2019)
IPFS ●Reliable and quick sharing of
medical data
●Requires proficient management of EHR on
clouds
Huang et al.
(2020)
Proxy re-
encryption
based model
●Minimal execution time
●Ensures confidentiality
●No optimization on the implementation
process
Yue et al.
(2016)
HGD architecture ●Patients are aware of who is
accessing their data
●Simple regulatory decisions
regarding patient data collection
and sharing
●Further optimization concepts are needed
for the effective management of data
Dwivedi
et al.
(2019)
Secure Hash
Algorithm
●Very much secure
●Offers better privacy
●Need consideration on DDOS attacks
Kuo et al.
(2020)
Randomized
Kaczmarz
algorithm
●Do not require knowledge of user
channel vectors
●Achieved better performance
and reduced computational
complexity
●Difficult to implement simple linear
detector
●For higher values, it requires more iterations
4G. VERMA
In the presented work, the blockchain with optimal encryption is used. Improved Blowsh-based
encryption is carried out here, which chiey targets secured data communication among the entities.
Rather than deploying the public key of an individual user for encryption, the public key of a specic
role is deployed for encryption. Furthermore, a private key is exploited for decryption. Here, the
optimal key generation is carried out by a new EHO-OBL algorithm. Moreover, the reverse process
based on a private key is done during the process of data decryption. Figure 1 shows the KSIBC
model for health data.
Improved blowsh algorithm with optimal key generation
Bruce modelled blowsh as a free, fast substitute to conventional encryption approaches. It is
gradually attaining recognition as a stronger encryption approach.
The Blowsh approach has numerous benets. It is ecient and appropriate for hardware
execution and no licence is necessary (Agrawal & Mishra, 2012). The basic operators of the blowsh
approach consist of XOR, addition and table lookup.
Some stipulations of blowsh approach are given in the below lines:
Doctor X
Patient B
Health
DB
ACL
1. Request to data
access by specifying
valid ID and patients
private key
Local DB
2. Saved in local DB
Application
gateway
Aggregator
Core
Block chain
3. Signature token
verified with extender
4. Allow to access
medical record in DB
5. To authenticate user,
session key is send to
doctor
5. Session key
send to EHR
EHR system
6. Data is sent to
doctor
2. Request to
verify user in ACL
Front end Back end
Optimal key
generation via
EHO-OBL
model
Improved
Blowfish
based
encryption
Figure 1. Diagrammatic representation of KSIBC model for health data.
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 5
●Contains 64 bit blocks cipher with an uneven key length.
●Contains four 32-bit P-boxes and S-array. The S-array includes 18 of 32-bit sub-keys, whereas
every P-box includes 256 entries.
●The approach includes 2 elements: “a key-expansion part and a data-encryption part”.
●The input is a data element of 64 bits.
The F operation deploys the substitution boxes, where there are 4, every one comprising 256 32-
bit entries (Meyers & Desoky, 2008). Conventionally, if the block XL is split to 8-bit blocks a;b;c;d
then the function FðXLÞis specied by Eq. (1). However, the modied blowsh model, FðXLÞis
formulated as shown in Eq. (2) and Eq. (3). Moreover, 64 bits of data are separated into four blocks
with 16 bits each block. As per the developed work, 128 bits data is separated into four blocks with
32 bits and the key size varies from 32 bits to 640 bits.
FðXLÞ ¼ ððP1;aþP2;bmod 2^32Þ P3;cÞ þ P4;dmod 2^32 (1)
FðXLÞ ¼ ððP1;aþP2;bÞmod 2^32Þ ððP3;cþP4;dÞmod 2^32Þ(2)
FðXLÞ ¼ ððP1;aÞ ðP2;bÞÞ þ ððP3;cÞ ðP4;dÞÞ mod 2^32 (3)
Proposed EHO-OBL algorithm for generating the optimal key
Solution encoding and objective function
In this work, the keys H are optimally chosen for attaining secured privacy preservation. For
optimisation purposes, a new EHO-OBL model is introduced in this work. The input solution to the
adopted scheme is illustrated in Figure 2, wherein, u represent the entire count of keys. The objective
function of the developed model denoted by Obj is given in Eq. (4), wherein KBRsignies the key
breaking time.
Obj ¼MaxðKBRÞ(4)
Proposed EHO-OBL algorithm
EHO (Wang et al., 2015) is a well-known optimisation model with a better convergence rate for
solving complex optimization problems. However, for progression of the searching quality, certain
modications are required and this work introduces a modied version of the EHO algorithm with
new tness-based computation. Moreover, Opposition Based Learning (OBL) is employed into the
developed model. Generally, self-improvement is conrmed to be capable in conventional optimiza-
tion models (Halbhavi et al., 2019; Jadhav & Gomathi, 2019; Rajakumar & George, 2012; Rajakumar,
2013a, 2013b; Swamy et al., 2013; Wagh & Gomathi, 2019). The procedure of the proposed EHO-OBL
model is as follows: Elephants are societal creatures that live in social groups, including calves and
females. The group involves a variety of clans, and a matriarch heads all clans. Usually, female
elephants live with the clans, whereas male elephants leave the clans when they grow up. The below
assumptions are considered in EHO-OBL.
1
H
2
H
u
H
....
Figure 2. Solution encoding.
6G. VERMA
(1) The population includes numerous clans and each clan includes female and male elephants.
(2) Some of the male elephants leaves the clan and live alone
(3) Each clan is headed by a matriarch.
This work deploys OBL that is modelled to utilise original individuals and their opposites. Therefore,
the points and their opposite are simultaneously computed to carry on with the best one. The OBL-
based initialisation ensures a better convergence rate, thus improving solutions speedily.
Clan-updating operator
As per the nature of the elephants, the elephants in a clan are led by the matriarch. Thus, the
matriarch chas a major impact on the novel positions of all the elephants. As per the presented
model, if the previous tness ðPFÞis greater than the current tness ðCFÞ; For every elephant in clan c,
a matriarch aects the subsequent position c. Thus, the elephant in the clan is updated as per Eq. (5).
Here, Zc;jand Zn;c;jpoints out the old position and new position of elephant j in clan c, correspond-
ingly. Zbest;cis the matriarchcthat indicates the best elephant in clan c.
Zn;c;j¼Zc;jþα:r:ðZbest;cZc;jÞ(5)
However, the best elephant in each clan could not be updated as per Eq. (5). It can be updated as
per Eq. (6), whereβ lies between 0 and 1 and Zcenter ;c points out the centre of clan c.
Zn;c;j¼βZcenter;c(6)
Separating operator
The separating procedure, in which the male elephants depart their family group, is modelled into
a separating operator. Conventionally, the separating operator is updated based on the lower and
upper bounds of elephant positions. However, as per the adopted model, the separating operator is
computed based on the best and worst positions as shown in Eq. (7), where Zworst;cindicate the worst
elephant individual of clan c,Zbestand Zworstdenotes the best and worst positions, respectively, di points
out the distance and randn represents the normal distribution between 0 and 1. Algorithm 1 reveals the
pseudo-code of the presented EHO-OBL model.
ALGORITHM 1: Pseudocode of EHO-OBL method
Initialization
Compute the fitness as per Eq. (20)
Repeat
Arrange all the elephants according to their fitness
Clan updating
For c¼1 to nclan (for each clan of elephant population) do
For j¼1 to nc (for every elephant in the clan c) do
If Zc;j¼Zbest ;ci then
Update Zc;j and generate Zn;c;j by Equation (6)
else
UpdateZn;c;j by Equation (5)
End if
End for j
End c
Separating operator
For c¼1 to nclan (for every clan of elephant population) do
Replace the clan with the worst elephant based on the best and worst positions as per Equation (7)
End for c
Evaluate population by the newly updated positions
Until (Maximum number of generations)
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 7
Zworst;c¼Zbest þ ðZbest Zworst þ1Þ randn (7)
Results and discussion
Simulation procedure
The developed secured privacy preservation model for health care in the cloud using theEHO-OBL
approach was executed in Python and outcomes were achieved. Accordingly, the performance of
the developed model was measured over existing models such as blowsh (Agrawal & Mishra, 2012),
RSA (Sharma et al., 2019), Advanced Encryption Standard (AES; Nandan & Rao, 2020), Elliptic-Curve
Cryptography (ECC; Sowjanya & Dasgupta, 2020), Elephant Herding Optimization (EHO; Wang et al.,
2015), MFO (Mirjalili, 2015) and Whale Optimization (WOA; Mirjalili & Lewis, 2016) models. Moreover,
the superiority of the presented model was validated in terms of key generation time, encryption
time and decryption time for varied le sizes such as 10 kb, 20 kb, 30 kb and 40 kb. Moreover, attack
analysis was done in terms of ciphertext attack and brute force attack.
Dataset description
The dataset used for the evaluation of the study is downloaded from https://archive.ics.uci.edu/ml/
datasets/heart+disease. Although there are 76 attributes in this database, all published studies only
use a subset of 14 of them. Till now, the Cleveland database is the only one that ML researchers have
used. The aim of this eld refers the presence of heart disease in the patient. It has a value of 0 (no
presence) to 4 (present). Experiments with the cleveland database have focused on attempting to
dierentiate the presence (values 1,2,3,4) from (value 0).
Attack analysis
In this section, the time taken for attacks by developed work is distinguished over the extant models,
particularly for the attacks such as ciphertext attacks and brute force attack. Accordingly, the time
taken to carry out ciphertext attack is revealed in Figure 3, where, the time taken to carry out brute
force attack is revealed in Figure 3. The time taken to carry out attacks was examined for varied key
sizes that range from 2, 4, 8, 16, 32, 64, and 128. On analyzing both the graphs, the presented EHO-
OBL model has taken more time to carry out attacks than compared schemes. In particular, more time
to for carrying out attacks promises the enhanced performance of the model. From Figure 3(a), the
presented EHO-OBL model for the adopted model has achieved more time to carry out ciphertext
attack, which is 69.96%, 57.14%, 33.33%, 68.13%, 23.44%, 39.93% and 69.96% better than Blowsh,
RSA, AES, ECC, EHO, MFO and WOA models when the key size is 2 bits. Accordingly, from Figure 3(b),
the time taken to carry out brute force attack for the adopted model is more, whereas the compared
models like Blowsh, RSA, AES, ECC, EHO, MFO and WOA models has acquired minimal time values.
Altogether, the performance of the proposed EHO-OBL work has been proved over other models.
Error analysis: proposed vs. conventional approaches
The performance of the adopted scheme (EHO-OBL) over the existing schemes for varied le sizes
such as 10 kb, 20 kb, 30 kb and 40 kb is described in this section. Accordingly, analysis was performed
for the adopted scheme over existing models such as Blowsh, RSA, AES, ECC, EHO, MFO and WOA.
On noticing the analysis outcomes, the proposed model has attained minimum time duration when
evaluated over prevailing schemes. In particular, reduced time duration guarantees the developed
model’s enhanced performance. More particularly, on considering the key generation time from
Table 2, the adopted model for le size of 10 kb has attained less value, and it is 51.04%, 91.48%,
92.64%, 91.48%, 89.99%, 91.06% and 91.48% better than traditional Blowsh, RSA, AES, ECC, EHO,
8G. VERMA
MFO and WOA models respectively. Similarly, on considering the encryption time from Table 3, the
implemented model seems to attain minimal values than the developed EHO-OBL model for all le
sizes. That is, the adopted approach forle size of 60 kb is 46.42%, 37.19%, 31.79%, 4.82%, 36.97%,
94.29% and 92.73% better than traditional Blowsh, RSA, AES, ECC, EHO, MFO and WOA models
respectively with less time values.
While focusing the decryption time from Table 4, the presented scheme has accomplished
minimal time values for all le sizes. Specically, for a le size of 60 kb, the suggested model is
85.24%, 67.92%, 69.56%, 66.84%, 66.84%, 80.76% and 89.85% better than traditional Blowsh, RSA,
AES, ECC, EHO, MFO and WOA models respectively. Therefore, the analysis proved the supremacy of
the proposed work in attaining minimal time duration.
(a)
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Time (sec)
Methods
2
4
8
16
32
64
128
(b)
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Time (Sec)
Methods
2
4
8
16
32
64
128
Figure 3. Analysis of developed approach over existing approaches for varied types of attacks, namely (a) Ciphertext attack (b)
Brute force attack.
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 9
Convergence anlaysis
The convergence of the proposed EHO-OBL method, over the existing work, is evaluated by varying
the count of iterations from 0 to 100, respectively. The result attained for the proposed and the
existing model is shown in Figure 4. At the 60th iteration, the EHO-OBL method is 25.34%, 16.34%,
and 33.94% better than the cost function recorded by EHO, MFO, and WOA, respectively. Therefore,
from the overall evaluation, it is vivid that the proposed EHO-OBL had recorded the least cost
function.
Key Sensitivity analysis
The result obtained for key Sensitivity analysis by varying the le size for 16, 24, and 32 is shown in
Table 5.
Table 2. Key generation time of proposed model over existing models for varied file sizes.
File size Blowfish (Agrawal & Mishra, 2012) RSA AES ECC EHO MFO WOA EHO-OBL
10 kb 0.1224 0.70313 0.8138 0.70313 0.59896 0.67057 0.70313 0.05993
20 kb 0.5599 0.12305 0.13574 0.15169 0.3097 0.21484 0.8138 0.06793
30 kb 0.67057 0.21484 0.26693 0.20182 0.46693 0.46693 0.70313 0.0763
40 kb 0.14616 0.86914 0.69987 0.75195 0.75521 0.59896 0.59896 0.0963
50 kb 0.12923 0.37109 0.31901 0.23438 0.26042 0.46693 0.070313 0.1693
60 kb 0.10449 0.49154 0.52083 0.48828 0.048828 0.67057 0.8138 0.26693
Table 3. Encryption time of proposed model over existing models for varied file sizes.
File size Blowfish (Agrawal & Mishra, 2012) RSA AES ECC EHO MFO WOA EHO-OBL
10 kb 0.1224 0.070313 0.08138 0.070313 0.059896 0.1224 0.067057 0.05599
20 kb 0.14616 0.12305 0.13574 0.15169 0.1097 0.70313 0.21484 0.037109
30 kb 0.067057 0.021484 0.026693 0.020182 0.026693 0.8138 0.46693 0.031901
40 kb 0.12923 0.086914 0.069987 0.075195 0.075521 0.70313 0.50182 0.023438
50 kb 0.05599 0.077109 0.071901 0.063438 0.076042 0.89896 0.66693 0.026042
60 kb 0.10449 0.089154 0.082083 0.058828 0.088828 0.98224 0.77057 0.05599
Table 4. Decryption time of proposed model over existing models for varied file sizes.
File size Blowfish (Agrawal & Mishra, 2012) RSA AES ECC EHO MFO WOA EHO-OBL
10 kb 0.33909 0.23348 0.25669 0.23348 0.18152 0.33909 0.25293 0.022802
20 kb 0.37188 0.3337 0.34111 0.37992 0.29304 0.23348 0.084414 0.01429
30 kb 0.25293 0.084414 0.10893 0.076335 0.11559 0.25669 0.10893 0.01473
40 kb 0.34881 0.26438 0.20092 0.20095 0.20306 0.23348 0. 26335 0.026588
50 kb 0.22802 0.1429 0.11473 0.086588 0.094708 0.18152 0.31559 0.034708
60 kb 0.31148 0.14335 0.15107 0.13869 0.13869 0.23909 0.45293 0.04599
Table 5. Key sensitivity analysis.
File size Key 1 Key 2 Key 3 Key 4 EHO-OBL
16 0.397366 0.421461 0.429285 0.418999 0.896465
24 0.419286 0.439994 0.453433 0.439452 0.920393
32 0.471658 0.466684 0.454611 0.426306 0.922965
10 G. VERMA
Conclusion
This paper had developed a new privacy preservation model using the EHO-OBL algorithm. Here,
blockchain with optimal encryption was carried out via an improved blowsh model that guarantees
authentication features. Further, optimal key generation was carried out using a new EHO-OBL
algorithm. Thus, the data integrity was maintained by the developed block-chain approach. At
last, the superiority of oered scheme was established over the conventional schemes regarding
diverse measures. Predominantly, the key generation time of the adopted model for le size of 10 kb
has attained less value, and it was 51.04%, 91.48%, 92.64%, 91.48%, 89.99%, 91.06% and 91.48%
better than traditional Blowsh, RSA, AES, ECC, EHO, MFO and WOA models respectively. Similarly, on
considering the encryption time, the implemented model seems to attain minimal values than the
developed model for all le sizes. That is, the adopted approach forle size of 60 kb was 46.42%,
37.19%, 31.79%, 4.82%, 36.97%, 94.29% and 92.73% better than traditional Blowsh, RSA, AES, ECC,
EHO, MFO and WOA models respectively with less time values. Therefore, the supremacy of the
introduced approach has been conrmed eectively. In the future, an investigation will be carried
out to show how blockchain technology can be used to improve eHealth systems. Integrating
blockchain technology into eHealth systems has the potential to increase service quality.
Nomenclature
Abbreviation Description
ACL Access Control List
AES Advanced Encryption Standard
EHRs Electronic Health Records
EHO Elephant Herding Optimization
ECC Elliptic-Curve Cryptography
HGD Healthcare Data Gateway
IoMT Internet Of Medical Things
IPFS Interplanetary File System
ICS Indicator-Centric Schema
KSIBC Keyless Signature Infrastructure
MFO Moth-Flame Optimization
MCC Mobile Cloud Computing
OBL Opposition Based Learning
Figure 4. Convergence analysis.
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 11
RSA Rivest – Shamir–Adleman
SEHRTB Secure and Ecient Health Record Transaction Utilizing Block Chain
WOA Whale Optimization
IoT Internet of things
TP-EHR Tamper-Proong-Electronic Health Records
Disclosure statement
No potential conict of interest was reported by the author(s).
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