Journal of King Saud University – Computer and Information Sciences 34 (2022) 2343–2358
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Computer and Information Sciences
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A multi-objective privacy preservation model for cloud security using
hybrid Jaya-based shark smell optimization
Danish Ahamad a,⇑, Shabi Alam Hameed b, Mobin Akhtar c
a
Department of Computer Science, College of Science & Arts, Sajir, Shaqra University, Saudi Arabia
Department of Computer Science, Collage of Science & Humanities, Huraymla, Shaqra University, Saudi Arabia
c
Department of Basic Sciences, Riyadh Elm University, Saudi Arabia
b
a r t i c l e
i n f o
Article history:
Received 8 August 2020
Revised 17 October 2020
Accepted 17 October 2020
Available online 22 October 2020
Keywords:
Cloud security
Privacy preservation
Optimal key generation
Jaya-based shark smell optimization
Hybrid optimization
Multi-objective function
a b s t r a c t
The rising volume of sensitive and personal data being harvested by data controllers has increased the
security essentials in the cloud system. The cloud module is not used just to store the data, but also to
process them on cloud premises. Security for the cloud premises is essential as the cloud has lot of outsourced, unprotected sensitive data for the public access. This has resulted repeated data violations, and
thus there is a need for the advanced legal data protection constraints. Various studies were conducted to
adopt the privacy preservation in the cloud, and most of the state-of-the-art techniques fail to handle the
optimal privacy when dealing with sensitive data, as it requires separate data sanitization and restoration
models. To overcome this challenge, this paper tempts to develop the privacy preservation model in the
cloud environment using the advancements of artificial intelligent techniques. Artificial Intelligent capabilities are working in the business cloud computing environment to make organizations more efficient,
strategic, and insight-driven. However, by hosting the data, cloud computing offers businesses high flexibility, agility, and cost savings. The two main phases of the proposed privacy preservation system are the
data sanitization and restoration. Moreover, the proposed sanitization process depends on the optimal
key generation, which is performed by the hybrid meta-heuristic algorithm. This hybrid algorithm merges
two well-performed algorithms, such as Shark Smell Optimization (SSO) and Jaya Algorithm (JA), and
thus termed as Jaya-based Shark Smell Optimization (J-SSO). The optimal key generation is accomplished
by deriving a multi-objective function that involves the parameters, such as the degree of modification,
hiding ratio, and information preservation ratio. Finally, the performance analysis has proved the efficiency of the proposed model over the state-of-the-art models in enhancing cloud security.
Ó 2022 Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the
CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction
The Cloud computing sector offers a massive amount of support
to the global environment in diverse areas like education, medical,
and business (Alabdulatif et al., 2017). Security is a major part of
the services that are offered around the globe. Data security plays
an important role in a cloud network environment. Numerous
types of security risks like security applications, key management
and encryption, access and identity management, audit scheduling,
⇑ Corresponding author.
E-mail address: danish.ahamad@gmail.com (D. Ahamad).
Peer review under responsibility of King Saud University.
Production and hosting by Elsevier
and physical and user access control (Tysowski and Anwarul
Hasan, 2013; Chun-TaLi and Chun-ChengWang, 2018) are listed
in the cloud data. In recent days, the encryption of data is carried
out using several encryption algorithms, which have the ability
for the conversion of text into a novel structure known as ciphertext. This is an encrypted type of the specified input as plain text
that will not be read by unauthorized users. Likewise, by using a
separate key, the encrypted data generates the algorithm to
decrypt the data that can able to offer the original text to the
authorized user (Li et al., 2017).
In cloud environments, privacy maintenance comprises two
aspects like data storage security and data processing security.
Data storage security includes the problems of promising user data
confidentiality when the data is saved in the data center. Data processing security includes the problems of how to preserve user
confidentiality at runtime in a virtualized cloud platform. Numerous approaches are developed for the privacy preserving in the
https://doi.org/10.1016/j.jksuci.2020.10.015
1319-1578/Ó 2022 Published by Elsevier B.V. on behalf of King Saud University.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
D. Ahamad, S. Alam Hameed and M. Akhtar
Journal of King Saud University – Computer and Information Sciences 34 (2022) 2343–2358
Nomenclature
NN
Neural Network
KPA
Known Plain Attack
J-SSO
Jaya based Shark Smell Optimization
PPR
Privacy-Preserving Rate
CC
Computational Complexity
SAW
Simple Additive Weighting
QoS
Quality of Service
MCDM
Multiple Criteria Decision Making
SFGA
Shuffled Frog Leaping Algorithm and Genetic Algorithm
BS-WOA Brain Storm based Whale Optimization Algorithm
PSV-GWO Particle Swarm Velocity aided Grey Wolf Optimization
GMGW Genetically Modified Glowworm
OI-CSA
Opposition Intensity-based Cuckoo Search Algorithm
Abbreviations Descriptions
FHE
Fully Homomorphic Encryption
SMC
Secure Multi-Party Computations
ABSO
Attribute-Based Signature Outsourcing
CSP
Cloud Service Providers
SKMA-SC Suppressed K-Anonymity Multi-Factor Authentication Based Schmidt-Samoa Cryptography
IoT
Internet of Things
CS
Cloud Server
IDP
IoT-Oriented Data Placement
NSGA
Non-Dominated Sorting Genetic Algorithm
SSO
Shark Smell Optimization
CPA
Chosen Plaintext Attack
JA
Jaya Algorithm
CDCs
Cloud Data Centers
there is a need for developing improved security model in the
cloud.
The major contribution of this paper is listed below.
cloud sector. In Xie et al. (2018), the privacy-aware access control
method has been proposed based on the Attribute Fuzzy Grouping
named PriGuarder for enhancing the data privacy in the cloud sector. Still, the performance of the authentication is not effective.
ABSO protocol is proposed in (Su et al., 2016) for protecting the
user’s security and privacy in the cloud environment. However,
the computational difficulty is extremely higher. The two main
conventional approaches are based on either perturbation or
cryptography approach. The perturbation-based method alters
the data with noise for providing the protection (Belguith et al.,
2018; Li, 2017). Conversely, the modification needs careful
calibration for locating the model privacy and usability for
improved stability. However, the privacy of the attributes is tackled with threats in security while dealing with plaintext. Perhaps,
cryptography-based method is dependent on the FHE (Jiang
et al., 2016) that has been advertized as a guaranteeing solution
recently. This allows the encrypted data along with the public keys
for uploading in the cloud service provider and SMC towards
generating the encrypted intermediary outcomes (Gao et al.,
2018; Mo et al., 2018). Currently, the data services provider may
not access any user record while it doesn’t contain the secrete keys
although it only provides as a computational platform (Liu et al.,
2017).
Privacy is still more complicated than security as it also needs
to hold through CSPs. The huge amounts of personal data are
exploited and analyzed by CSPs based on the cloud. If the CSP is
considered to be trusted then in the cloud, the sensitive data handling is simpler (Li et al., 2017; Stergiou et al., 2018). Still, various
legal problems are existing. However, the data subjects along with
their personal or healthcare data trust the data controller in various cases, although they don’t permit the controller toward transferring the data by the trustworthy members. In contrast, the CSP
may be under a jurisdiction different from the controllers. Moreover, to create the opportunity of legitimating the users’ data, various public CSPs provide their services at free cost. In recent years,
numerous solutions are proposed for solving the above mentioned
problems and to re-establish the user’s control and also for the
data protection outsourced to the cloud (Behl, 2011). All of them
entail masking sensitive data hence the protected values are saved
in the cloud and the protected values are unmasked only by the
user or controller who owns the data that are retrieved from the
cloud. On the other hand, if the user needs to utilize both the
cloud’s computational power and cloud’s storage, then it is more
hard due to the protection of data, and it might be prepared
well-suited with the outsourced computations on masked data
on cloud premises (Ali et al., 2015; Choi and Lee, 2015). Therefore,
To implement the proposed privacy preservation model in the
cloud with various datasets, such as Air quality, Concrete data,
Heart disease, Super conductivity, and Whole sale customer
data.
To develop the efficient cloud data privacy preservation model
with the data sanitization and restoration stages and using an
optimal key generation approach.
To implement a new meta-heuristic algorithm called J-SSO with
the combination of JA and SSO. Here, the optimal key generation
is optimized using the proposed J-SSO algorithm with the
parameters like degree of modification, hiding ratio, and information preservation ratio.
This paper is sequentially ordered as follows, Section 2 discusses the literature review. Section 3 explains the privacy cybersecurity model adopted for cloud data. Section 4 describes the
data sanitization and restoration with optimal key generation. Section 5 defines the objective model derived for the developed cloud
data cyber security. Section 6 discusses the experimental setup,
and Section 7 concludes this paper.
2. Literature review
2.1. Related works
In Praveena and Rangarajan (2018) have developed a new cloud
service approach based on the integration of both public and private cloud and it was named as hybrid cloud service. Currently,
for the cloud environment, the challenging issue was considered
as a security due to hybrid cloud combination approach. In the literature, various security techniques were presented and these
were not obtained at the adequate security level. Therefore, to
solve this security issue, a novel machine learning application
was developed to provide the security headed for the hybrid cloud
networks during accessing and storing or retrieving the data. A
new algorithm was implemented based on a dynamic access control mechanism called dynamic spatial role-based access control
algorithm that was developed from the combination of Enhanced
C4.5 and de-duplication processing algorithm. In addition, less
than 5% of cloud users were denied access in comparison with
the existing system and hence the security is enhanced by incorporating the cryptographic algorithm. It can be observed that the
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Journal of King Saud University – Computer and Information Sciences 34 (2022) 2343–2358
output was measured. Hence, the proposed framework had established better results based on MSCryptoNet.
In Yan et al. (2020) offered the design and implementation of a
node controller intended for a cloud energy storage network. For
ensuring the security of the communication network, the system
deployment procedures and function division were executed
regarding the cloud energy storage scheme. Safety protection metrics were offered for the communication network demands that
permitted the model for running stably and safely. The distribution
capacity of the community was 1,260 kVA and the maximum load
was ~ 250 kW. The total charging and discharging power of the
energy storage equipment was ~ 90 kW and the permeability of
the energy storage installation was 36%. After the installation of
energy storage, the error rate of the load was decreased by
27.6%. Finally, results showed that the installation of energy storage can significantly reduce the load and differential rate.
In Li et al. (2019) have implemented a new trust assessment
structure named ‘‘STRAF” for the reputation and security of cloud
services. This structure was facilitated by the trust assessment of
cloud services so as to certify the cloud-based IoT context security
through the reputation-based and security-based trust assessment
approaches. The security of a cloud service was evaluated by using
the cloud-specific security measures based on the security-based
trust assessment approach. In the reputation-based trust assessment approach, the reputation of a cloud service was evaluated
on the quality of cloud service by exploiting the feedback ratings.
The QoS and reputation were typically represented by a comprehensive score reflecting the overall quality and opinion or by a
small number of scores on several major aspects of performance.
Experiments conducted using a synthesized dataset of security
metrics and a real-world web service dataset show that the proposed trust assessment framework can efficiently and effectively
assess the trustworthiness of a cloud service while outperforming
other trust assessment methods.
In Ali et al. (2020) have ‘‘reviewed the ISO 27002 Information
Security standard” in the existing literature and established that
individual awareness, compliance matters, and operational security was created for important government problems other than
the process-oriented and the often-highlighted technical cloud
security constraints. Theoretical cloud computing security constraint system was developed through four components such as
compliance and legal requirements, data security, risk assessment,
and technical and business requirements on cloud security so as to
encourage a balanced view for governments. The cloud services
were adopted by using model, hence the governments can employ
jointly for demand uniform security constraints. Results from a
comprehensive industry survey showed that 74% of participants
highlighted that security was a major reason behind the low level
of adoption of the cloud computing service model.
In Sengupta et al. (2020) have proposed secure Fog-based
advent of IIoT by suitably plugging a number of security features
and by offloading some of the tasks judiciously to fog nodes. These
features secure the system alongside by reducing the trust and
burden on the cloud and resource-constrained devices. The benefits include scalability, agility, efficiency and decentralization. On
the other hand the potential of fog computing was widespread as
it reduced latency thereby reducing cost, and also provide better
security. It consists of several limitations, huge computation and
bandwidth cost to the data. Moreover, sharing the same data with
multiple users require the sender to encrypt the data individually
for each user.
In Thanga Revathi et al. (2019) have introduced BS-WOA for
identifying the secret key. It uses the fitness function for deriving
the secret key, such that the privacy and the utility of the data were
maintained as high as possible. One of the major advantages was
that it had low number of parameters and lack of local optima
performance of the proposed method has performed well when
compared with the existing algorithm
In Mohana Prabha and Vidhya Saraswathi (2020) have developed a new multifactor authentication known as SKMA-SC
approach. This approach has included three key procedures such
as authentication, data access, and registration. At the registration
stage, the clients were registered with their individual identification data and securely save the information in CS by using suppression method. In a cloud environment, this was helped by SKMA-SC
approach for preserving the client’s sensitive data from third parties or adversaries. At the authentication stage, the client’s identity
was validated by the SKMA-SC approach regarding multi-factors
like conditional attributes, one-time token, and password. Finally,
during the data access stage, SKMA-SC approach permitted the clients for acquiring the requested data services while they were
authorized for executing the Schmidt-Samoa data encryption or
decryption procedure. By considering these stages, SKMA-SC
approach was used to avoid the unauthorized access from the
illegal party insecure communications in a cloud environment.
SKMA-SC technique improves the PPR by 20% and 42% as compared
to existing methods. The experimental result proves that
SKMA-SC technique enhances the PPR and lessens the CC
authentication.
In Xiaolong et al. (2018) have developed an IDP approach with
privacy preservation. Firstly, in the cloud data center, the fat-tree
topology was analyzed with the data access time, energy consumption, and resource utilization. Further, by concerning IDP approach,
the NSGA-II was implemented for achieving the energy saving, proficient high data access, and resource usage. In the meantime, privacy preservation of the IoT data was realized. Here, 50
experiments were conducted in the case of convergence for each
dataset scale and multiple sets of results were observed. In order
to pick out a relatively best solution, they had used SAW and
MCDM to construct the utility function. Hence, the experimental
analysis had proven the effectiveness and efficiency of the proposed method.
In Tian et al. (2019) have presented a tailor-made public auditing scheme in fog-to-cloud based IoT scenarios, for data storage
that could obtain the entire necessary performance and security
constraints. In particular, a tag-transforming scheme based on
the bilinear mapping technique was developed for converting the
tags that were produced by mobile sinks to the ones formed using
the fog nodes in the proof generation phase. It cannot efficiently
protect the privacy of the identity, but in the verification phase,
it also reduced the costs of communication and computation. Further, a zero-knowledge proof approach was designed for verifying
the IoT data integrity from various generators whereas; it attained
the exact data-privacy preserving. The results demonstrate that it
can efficiently achieve secure auditing for data storage in fog-tocloud based IoT scenarios. From the experimental analysis, the proposed approach outperformed in terms of energy consumption,
computation and communication costs in fog-to-cloud based IoT
scenarios.
In Kwabena et al. (2019) developed an MSCryptoNet, which was
dependent on the multi-scheme FHE. This was implemented for
the approximation of the activation function that was used in the
CNN through low degree polynomials. This was important in the
homomorphic encryption systems for the computations. The proposed system was targeted with the following cases such as (a)
the encrypted inputs were practically estimated by the classifier
with probable diverse encryption methods or even various keys
whereas securing entire operations together with intermediate
outcomes and (b) the reduction of computational cost and communication costs of the data providers. It measured the accuracy of
the model for classification over encrypted data. The encrypted
inputs were given to the trained network and the accuracy of the
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Journal of King Saud University – Computer and Information Sciences 34 (2022) 2343–2358
verification as well as the public audit ability. It also attains
the complete security needs and the indispensable performance.
But, the load balancing of various auditors is not handled and it
also does not realize the effective auditing for the big data.
Multi-scheme FHE (Kwabena et al., 2019) is encrypted with several keys or schemes and it can be used to train on various datasets and also the NN are relieved from efficiency as well as
accuracy loss. Still, it cannot be applied practically. Network
security protection technology (Yan et al., 2020) enhances the
system load characteristics and runs the system stably and
safely. It also provides better valley filling and peak-clipping
effects. Yet, various risks related to the accounts management,
security, and data protection occur. Security-based trust assessment method (Li et al., 2019) obtains the quantitative trustworthiness of the cloud services and the reputation. Here, the
security is provided by the STRAF as complementary features.
But, it cannot be performed in the real world and a working prototype is not constructed by this trust assessment framework.
Conceptual cloud computing security requirements model (Ali
et al., 2020) reduces the operational risks related to the privacy
and security issues and using cloud computing security model,
more benefits are provided to the government. Since the findings
cannot be applied to regional local governments in various distinct countries with the same socio-economic conditions and it
also cannot be applied to various sectors, industries, or countries.
These challenges motivate to find a novel method for providing
security in the cloud-related applications.
entrapment, in solving clustering problems. The disadvantage was
that the presence of a large number of users in the data pool, and
hence, it was extremely difficult to maintain the privacy of every
database.
In Jyothi Mandala and Chandra Sekhara Rao (2019) have introduced PSV-GWO to find the optimal key. The key was optimally
produced by the PSV-GWO method for data sanitization process.
It was not easy to fall into the local optimum compared with other
intelligent algorithms and had less parameter. It also poses some
limitations, such as slow convergence, bad local searching ability,
and low solving precision.
In Annie Alphonsa and Amudhavalli (2018) have proposed
GMGW to perform the sanitization process. It was used to perform
the sensitive healthcare data sanitization and restoration process.
It had significant benefits like consumption cost, application hosting, content storage as well as delivery. It can converge prematurely and be trapped into a local minimum especially with
complex problems.
In Shailaja and Rao (2019) have developed OI-CSA to find optimal key generation. It involves two phases, namely, data sanitization and data restoration. In both the sanitization and restoration
processes, key extraction play a significant role, which was
selected optimally using opposition intensity-based Cuckoo Search
Algorithm, which was the modified format of Cuckoo Search Algorithm. It provided better runtime and scalability. However, there
was no consideration on combination with web mining.
The SSO has many significant benefits like; good convergence
acceleration, fitting for wide search space, powerful neighborhood
search characteristic, higher feasibility and efficiency in producing
global optima. The advantage of JA includes faster computational
speed, fast searching algorithm, and more memory storage. The
drawback of SKMA-SC (Mohana Prabha and Vidhya Saraswathi,
2020) is that it does not enhance the confidentiality and data integrity level for different techniques in the cloud. The limitation of
Public auditing scheme (Tian et al., 2019) include, that it is does
not handle the load balancing of various auditors. The disadvantage of Multi-scheme FHE (Kwabena et al., 2019) is that it cannot
be applied in a practical manner. The Security-based trust assessment method (Li et al., 2019) does not construct a working prototype and cannot perform in a practical cloud environment.
3. Proposed cybersecurity model adopted for cloud data
3.1. Developed architectural view
Security in cloud computing is an important criterion to be
addressed in the current researches. The data will be at high risk
when the security measures are not offered correctly for data
transmissions and operations. As cloud computing offers the ability for accessing the stored data to a set of users, there is a chance
to have high risk for the data processing. Efficient security measures are required to be developed by identifying the security challenge and solutions for handling security limitations in cloud. If
numerous organizations are sharing the resources then, there is a
possibility of data misuse. Therefore, for avoiding the risk, it is necessary to protect the data archives and also the data, which entails
process, transit, or storage. For avoiding the various limitations in
the data security schemes in the literature, a novel cyber security
model is designed for the cloud data. The architectural illustration
of the proposed privacy preservation model for the cloud data is
shown in Fig. 1.
The proposed cyber security model uses the cloud data for analyzing the real time challenges. Datasets considered for the proposed cyber security model are Air quality, Concrete data, Heart
disease, Super conductivity, and Whole sale costumer data, and
these datasets are gathered from UCI repository. The two main
phases of the proposed privacy preservation system are the data
sanitization and data restoration. Data sanitization is the process
of hiding the sensitive data or information in a cloud and hence
intends to prevent it from leaking on to the unauthorized point.
Furthermore, the proposed sanitization process is done based on
the optimal key generation, and this process is improved by using
the proposed hybrid meta-heuristic algorithm called J-SSO. The
optimal key generation is regularized by considering a multiobjective function, which uses parameters such as information
preservation ratio, hiding ratio, and degree of modification. By
using this multi-objective function, the proposed J-SSO can efficiently execute the data sanitization and data restoration for cloud
2.2. Problem statement
Cloud security protects the data, services, virtualized IP, and
applications by means of a wide group of technologies, controls,
and policies. But, it has various drawbacks such as loss of control, data loss, unsecured application programming interfaces,
etc. There is a need to resolve these limitations in the future.
Some of the major features and challenges are listed in Table 1.
Machine Learning (Praveena and Rangarajan, 2018) restricts the
access of the data depending on time and space. The security
risks occurring in the hybrid cloud are minimized by machine
learners. The cloud users are restricted from accessing the cloud
data. Here, the secured storage is not provided using an effective
cryptographic algorithm. SKMA-SC (Mohana Prabha and Vidhya
Saraswathi, 2020) secretly stores the client’s personal information in the CS database. It also enhances the privacy-preserving
rate by avoiding illegal access in the cloud. Still, the confidentiality and data integrity level are not enhanced for different techniques in the cloud and also, it does not use the function with
signcryption. NSGA-II (Xiaolong et al., 2018) minimizes the
energy consumption of CDCs. The access performance and the
CDCs average resource utilization are also enhanced. Yet, it does
not enhance the privacy constraints of the real –time IoT data
and in the real physical environment, it cannot be implemented.
Public auditing scheme (Tian et al., 2019) obtains the blockless
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Journal of King Saud University – Computer and Information Sciences 34 (2022) 2343–2358
D. Ahamad, S. Alam Hameed and M. Akhtar
Table 1
Features and challenges of state-of-the-art cloud security models.
Author [citation]
Methodology
Praveena and Rangarajan (Praveena and Rangarajan, July
2018)
Machine Learning
Prabha and Saraswathi (Mohana Prabha and Vidhya
Saraswathi, 2020)
SKMA-SC
Shucun et al. (Xiaolong et al., 2018)
NSGA-II
Fulin et al. (Tian et al., 2019)
Public auditing
scheme
Zhen et al. (Owusu-Agyemang Kwabena; Zhen Qin ;
Tianming Zhuang ; Zhiguang Qin, ‘‘MSCryptoNet:
Multi-Scheme Privacy-Preserving Deep Learning in
Cloud Computing”, IEEE Access, vol. 7, pp., 2019)
Multi-scheme FHE
Jialiang et al. (Yan et al., 2020)
Network security
protection
technology
Qixu et al. (Xiang Li ; Qixu Wang ; Xiao Lan ; Xingshu
Chen ; Ning Zhang ; Dajiang Chen, ‘‘Enhancing CloudBased IoT Security Through Trustworthy Cloud
Service: An Integration of Security and Reputation
Approach”, IEEE Access, vol. 7, pp. 9368 - 9383,
January, 2019)
Shrestha et al. (Ali et al., 2020)
Security-based
trust assessment
method
Jayasree Sengupta et al. (Sengupta et al., 2020)
Homomorphic
Encryption
BS-WOA
S. Thanga Revathi et al. (Thanga Revathi et al., 2019)
Conceptual cloud
computing security
requirements
model
Jyothi Mandala and M. V. P. Chandra Sekhara Rao
(Mandala, 2019)
PSV-GWO
M. M. Annie Alphonsa and P. Amudhavalli (Annie
Alphonsa and Amudhavalli, 2018)
GMGW
G.K. Shailaja and C.V. Guru Rao (Shailaja and Guru Rao,
2019)
OI-CSA
Features
It minimizes the security risks
occurring in the hybrid cloud.
It restricts the data access on
the basis of space and time.
It restricts the cloud users from
accessing the cloud data.
It avoids the illegal access happening in the cloud thereby
enhancing the privacy-preserving rate.
It stores the personal information of the clients in the CS
database in a secret manner.
It enhances the access performance and the CDCs average
resource utilization.
It minimizes the CDCs consumption of energy.
It achieves all the security
needs and the indispensable
performance.
It attains blockless verification
and public auditability.
The NN are relieved from efficiency and accuracy loss.
It can be trained on various
datasets that are encrypted
with several keys or schemes.
It permits the system to run
stably and safely.
It enhances the system load
characteristics.
It provides better valley filling
and peak-clipping effects.
The STRAF provides both reputation and security as complementary features.
It attains the quantitative trustworthiness
of
the
cloud
services.
It provides more benefits to the
government by means of a
cloud
computing
security
model.
It reduces the operational risks
for privacy and security issues.
It reduces latency and provides
better security.
It has low number of parameters and lack of local optima
entrapment, in solving clustering problems.
It is not easy to fall into the
local optimum compared with
other intelligent algorithms.
It has less parameter.
Easy
to
run
parallel
computation.
Have higher probability and
efficiency in finding the global
optima.
It provides better runtime and
scalability.
Challenges
It does not provide secured storage by
means of an efficient cryptographic
algorithm.
It does not function with various cryptography techniques such as signcryption.
It does not enhance the confidentiality
and data integrity level for different techniques in the cloud.
This technique cannot be implemented in
the real physical environment.
The privacy constraints of the real-time
IoT data are not enhanced.
The effective auditing for the big data is
not realized.
It does not handle the load balancing of
various auditors.
It cannot be applied in a practical manner.
Various types of risks related to the
accounts management, security, and data
protection occur.
The trust assessment framework does not
construct a working prototype.
It cannot perform in a practical cloud
environment.
It is not applied to various sectors, industries, or countries.
The findings cannot be applied to regional
local governments having same socioeconomic conditions in various distinct
countries.
High computation
Bandwidth cost is high
It is extremely difficult to maintain the
privacy of every database.
Bad local searching ability and low solving precision.
Slow convergence.
It can converge prematurely and be
trapped into a local minimum especially
with complex problems.
Needs to improve privacy-preserving
data mining
3.2.1. Air quality
This dataset has 9358 instances of hourly averaged responses
from 5 metal oxide chemical sensors array that are embedded in
a device called an Air Quality Chemical Multisensor. The sensor
is positioned at a most polluted area in an Italian city. It contains
the recorded data from March 2004 to February 2005. It has various attributes like Time, reference analyzer, Absolute Humidity,
Temperature, Date, and Relative Humidity.
data. Data restoration is the process of revealing sensitive data by
using the unique key that is used for the data sanitation process.
3.2. Dataset Description
The proposed cyber security for the cloud data is done by gathering various datasets in UCI repository that are described below.
The considered datasets and their links are given in Table 2.
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Journal of King Saud University – Computer and Information Sciences 34 (2022) 2343–2358
3.2.4. Super conductivity
There are two files in the dataset, in which the features are
extracted from the 21,263 superconductors. The number of attributes in this dataset is 81. The critical temperature is predicted using
the extracted features. Multivariate dataset is considered here.
3.2.5. Whole sale costumer data
In this dataset, the number of instances and attributes are considered as 440 and 8, respectively. Some of the attributes are fresh,
grocery, frozen, milk, delicatessen, detergents_paper, region,
Oporto, Lisbon, etc.
4. Data sanitization and restoration with optimal key
generation
4.1. Data sanitization and restoration
Data sanitization is the process of hiding the sensitive data or
information in a cloud that intends to prevent the data from leaking on to the unauthorized point. On the other hand, restoration is
the reverse process of the data sanitization process, which is carried out for evaluating the sanitization effectiveness. The flow diagram of data sanitization and restoration is shown in Fig. 2.
During sanitization, the binary conversion is done for both the
cloud data and the key matrix generation, where the proposed JSSO is used for generating the optimal key. The obtained binary
data undergo XOR operation for achieving the sanitized data.
Hence, from the original cloud data and the key matrix generation,
the sanitized data is produced as mentioned in Eq. (1).
Ds0 ¼ Ds Key2
ð1Þ
0
In Eq. (1), the sanitized data is denoted as Ds , the original data
is represented as Ds, and the optimally generated key is termed as
Key2 . As given in the objective function of the proposed cyber security model, the items Di are there for performing sanitization, and
0
Di are achieved after sanitization process. Therefore, the sensitive
rules are hided in the sanitization process which is then transferred
to the cloud. Hence, the data will be protected for further use,
which can improve the performance of the security in cloud sector
Fig. 1. Data sanitization and restoration using an optimal key generation.
Table 2
Dataset description.
Datasets
Name of the
datasets
Links
Dtaset 1
Air quality
Dataset 2
Concrete data
Dataset 3
Heart disease
Dataset 4
Super
conductivity
Dataset 5
Whole sale
customer data
https://archive.ics.uci.edu/ml/datasets/Air
+Quality: Access Date: 11–07-2020
http://archive.ics.uci.edu/ml/datasets/
Concrete
+Compressive+Strength: Access Date:
11–07-2020
https://archive.ics.uci.edu/ml/datasets/Heart
+Disease: Access Date: 11–07-2020
‘‘https://archive.ics.uci.edu/ml/datasets/
Superconductivty+Data: Access Date:
11–07-2020”
‘‘https://archive.ics.uci.edu/ml/datasets/
Wholesale+customers: Access Date:
11–07-2020”
3.2.2. Concrete data
This dataset contains a number of instances as 1030, number of
attributes as 9, and attribute breakdown is divided into quantitative input variables as 8, and quantitative output variable as 1. Various variables such as Concrete compressive strength, Age, Blast
Furnace Slag, Fly Ash, Fine Aggregate, Cement, Water, Coarse
Aggregate, and Superplasticizer are used here.
3.2.3. Heart disease
This dataset includes a number of attributes as 75, and the
number of instances as 303. The attributes types like cigs, smoke,
htn, chol, age, id, etc are considered in the dataset.
Fig. 2. Solution encoding of key length using the J-SSO algorithm.
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Journal of King Saud University – Computer and Information Sciences 34 (2022) 2343–2358
D. Ahamad, S. Alam Hameed and M. Akhtar
Initialization of SSO Algorithm: Initially, odor particles are
found at this step. If the shark smells any odor particle then the
search process begins that is generally a fragile diffusion from prey
or injured fish. This behavior is modeled by initializing the population based on the random manner within the specific search area
that each of them denotes one odor particle. This establishes one
probable location for the shark at initial search process. This is
given in Eq. (5).
without any cyber attacks. During restoration, the original data is
recovered by using the same key using the proposed J-SSO. This
procedure is given in Eq. (2).
^ ¼ Ds0 Key
Ds
2
ð2Þ
^ represents the restored data.
Here, the term Ds
4.2. Proposed key generation
h
In the proposed cloud data cyber security model, the key extraction plays a major role in both data sanitization and restoration
process, where the optimization is done by the proposed J-SSO.
The solution transformation is the first step for the key generation
process, where the key Key is converted into a new form by Kronecker method. Here, Key is converted into Key1 by using Eq. (3),
pffiffiffiffiffiffiffiffi
in which the size is considered to be no00 Pmax . For example,
for the Key ¼ f4; 5; 1g, the key matrix is given in Eq. (3).
3
4 4 4
6
7
Key1 ¼ 4 5 5 5 5
ð5Þ
th
initial
S1j
position vector is denoted as
for the optimization problem,
where j ¼ 1; 2; :::; PS, and the initial candidate solution is given in
Eq. (6).
h
i
S1j ¼ s1j;1 ; s1j;2 ; ::; s1j;ND
th
In Eq. (6), the j
sented as
ð3Þ
ð6Þ
th
shark position of the k
dimension is repre-
th
th
s1j;1 , the k decision variable of the j individual is repre1
sj , where k ¼ 1; 2; :::; ND, and the term ND is the number
sented as
of decision variables in the optimization problems. In each position, the level of odor denotes their closeness to the fish or prey,
which is mathematically formulated via objective function in SSO
algorithm. A maximization problem is assumed without generality
loss, where, if the objective function has a higher value like a stronger odor then the closer location towards the prey is considered as
a better optimal candidate solution.
In Eq. (3), the number of transactions is denoted as no, the nearest highest perfect square of no is termed as no00 and the maximum
transaction length is mentioned as Pmax . Based on Eq. (3), the
reconstructed key matrix Key1 is produced by executing rowwise duplication. Furthermore, the key matrix Key2 is generated
by Kronecker method and it is given in Eq. (4).
Key2 ¼ Key1 Key1
i
Here, the population size is represented as PS. The j
2
ffi
1 1 1 ½pffiffiffiffiffi
no00 P max S11 ; S12 ; S13 ; :::; S1PS
ð4Þ
4.3.2. Movement of shark to prey
In each position, the shark moves towards the prey with a
velocity based on the stronger odor particles. Thus, related to the
position vectors, the initial velocity vector PS is given in Eq. (7).
Here, each velocity has elements in every dimension that is given
in Eq. (8).
In Eq. (4), the symbol represents the Kronecker product. The
pffiffiffiffiffiffiffiffi
size of Key2 is also considered as no00 P max . Here, the main contribution of the proposed cloud data security model is the optimization of key termed as Key using the proposed J-SSO algorithm.
1
Ve1 ; Ve12 ; ::; Ve1PS
4.3. Proposed J-SSO
A new hybrid J-SSO algorithm is developed for the optimal key
generation in the proposed privacy preservation model for cloud
data. In this work, the proposed algorithm has hybridized the beneficial concepts of SSO, and JA. The benefits of SSO include better
efficiency in solving the real-world optimization problems, provide
the efficient way of changing the parameters, and provide better
exploration ability at the initial searching process. The JA has many
significant benefits like; easy to solve multi-objective optimization
problems, find the optimal value in less time, and simple for developers. So, the proposed hybrid algorithm provides better performance than other bio inspired algorithms.
Ve1j ¼
h
v e1j;1 ; v e1j;2 ; ::; v e1j;ND
ð7Þ
i
ð8Þ
The direction of shark movement is changes based on the intensity of odor. If the concentration of odor is increased then the shark
velocity also increases. This movement behavior is mathematically
modeled with the gradient of the objective function. The direction
of movement is illustrated in Eq. (9) when the objective function is
enhanced via the highest rate.
Veij ¼ gi : r1: rðof Þsij
ð9Þ
Here, i ¼ 1; 2; :::; imax and the objective function of the conventional SSO is given as of and its gradient is represented as rðof Þ.
The term imax denotes the forward movement of shark, and it is
separated into a number of stages, where the stage number is
denoted as i. A constant value Veij is represented as the velocity
of shark in each stage. The term gi is limited in the boundary of
(Praveena and Rangarajan, 2018). A random number with uniform
distribution is denoted as r1 and its value is in the range of
(Praveena and Rangarajan, 2018). The velocity in each dimension
is given in Eq. (10).
4.3.1. Conventional SSO (Abedinia et al., 2014)
This is inspired by the searching behavior of shark towards their
prey source based on the smell sense. In the search environment,
the optimization procedure is created based on their search behaviors. Some hypothesizes are considered for the modeling of shark
search behavior. Firstly, (a) if some fishes are injured or leaking
the blood into the sea water or search environment, then the velocity of the fish mobility is low and the velocity of the shark’s is
neglected. Thus, the prey is approximately fixed. Secondly, (b)
when the blood is repeatedly infused to the sea water, then the
water floods on distorting the odor particles are ignored. Further,
if the closer odor elements are observed then the location of the
prey is stronger. Hence, by observing the odor particles, the prey
is approached by the shark. Thirdly, (c) there will be only one
injured fish in the search environment of the shark. The sequential
steps for the conventional SSO are given below.
Veij;k ¼ gi : r1:
@ ðof Þ i
sj;k
@sk
ð10Þ
The shark’s acceleration is limited because inertia of the shark.
Therefore, their current velocity is based on the previous velocity
that is shown in Eq. (11).
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D. Ahamad, S. Alam Hameed and M. Akhtar
v eij;k ¼ gi : r1:
@ ðof Þ i
sj;k þ ai : r2:v ei1
j;k
@sk
Journal of King Saud University – Computer and Information Sciences 34 (2022) 2343–2358
S0d;c;it ¼ Sd;c;it þ r1d;it Sd;best;it Sd;c;it r2d;it Sd;worst;it Sd;c;it ð11Þ
Here, the momentum rate or inertia coefficient is termed as ai
for range (Praveena and Rangarajan, 2018), and the value is a constant for stage i. Another random generator is r2 that is used to
increase the search diversity. Before starting the search process,
the initial velocity of shark is considered as v e1j;k for the first stage
velocity, where
v e0j;k
th
Here, the value of d variable for the best candidate is termed as
th
Sd;best;it and the value of d variable for the worst candidate is termed as Sd;worst;it . The updated value of Sd;c;it is termed as S0d;c;it and
th
the two random numbers for d variable through the iteration it
are termed as r1k;it and r2k;it , respectively in the range of
(Praveena and Rangarajan, 2018). In Eq. (16), the term leaning of
the solution to move nearer to the best solution is denoted as
r1d;it Sd;best;it Sd;c;it and the tendency of the solution to avoid
the worst solution is given as r2d;it Sd;worst;it Sd;c;it . When the bet-
is randomly fixed as small value or may be
neglected. The velocity of shark can be increased up to a limit.
The velocity limiter is used for the shark movement, which is given
in Eq. (12).
@ ðof Þ i
i i1 i1 ;
b
:
v
e
v ej;k ¼ min gi : r1:
sj;k þ ai : r2:v ej;k
j;k i
@sk
ð12Þ
ter function value is given then S0d;c;it is acknowledged. At the end of
iteration, all the function values are accepted and maintained.
These are given as the input for the next iteration.
Here, the velocity limiter ratio is termed as bi for stage i. The
magnitude of v eij;k is achieved using Eq. (12), where the ‘‘min”
4.3.4. Proposed J-SSO algorithm
The conventional SSO algorithm proves the better efficiency
in solving the real-world optimization problems. This algorithm
provides the efficient way of changing the parameters. It provides the better exploration ability at the initial searching process. However, it has limitations like it suffers from low
convergence speed and also requires high time for convergence.
These challenges may minimize the population range for escaping from the local optimal point. The aforementioned challenges
are considered for developing the new algorithm with the combination of JA. The conventional JA has various features such as
it is simple for developers and it has only one stage with few
parameters. The conventional JA is employed for the multiobjective optimization problems. It is able to find the optimal
value in less time. These features are considered for implementing the new algorithm called J-SSO. Here, in the proposed algorithm instead of rotational movement, JA updating is employed.
In this proposed algorithm, a new formula is formulated for the
gradient of the objective function of SSO termed as rðof Þ that is
given in Eq. (17).
operator is given as selected term. Due to the forward movement,
the position of shark is represented as Piþ1
and it is established
j
based on their previous position and velocity, which is given in
Eq. (13).
Piþ1
¼ Sij þ Veij : Dti
j
ð13Þ
Here, the time interval of stage i is denoted as Dti , which is
assigned as Dt i ¼ 1 for all stages for simplicity. Each component
of v eij;k where ð1 6 i 6 NDÞ of the vector Veij is achieved from Eq.
(12). For discovering the best candidate solutions, the shark develops a local search in each stage. This is formulated in Eq. (14).
Q jiþ1;m ¼ Piþ1
þ r3:P iþ1
j
j
ð14Þ
Here, the number of points in the local search of each stage is
indicated as M, where m ¼ 1; 2; :::; M. Another random number is
denoted as r3 with uniform distribution in the range of [1, +1].
The M point of the local search is Q iþ1;m
in the vicinity of Piþ1
and
j
j
these M points are connected for finding a closed contour equivalent to the rotational movement of shark. During the rotational
movement, if shark discovers a point with stronger odor then the
shark will move to that point and carry on the search path from
that particular point. This behavior is simulated in Eq. (15).
n o
Sjiþ1 ¼ arg max of Piþ1
; of Q iþ1;1
; :::; of Q iþ1;M
j
j
j
ð16Þ
rðof Þ ¼
abs of gbest of ðjÞ
of gbest
ð17Þ
Here, the term of gbest denotes the fitness function of the best
solution and the fitness function of the current solution is termed
as of ðjÞ. Hence, if the obtained solutions are reaching the value of
rðof Þ < 0:5 then the position update is done by SSO forward
movement or else the position update is done by JA. The pseudo
code of the J-SSO is depicted in Algorithm 1.
ð15Þ
The term Piþ1
is acquired from forward movement and from the
j
is obtained, and Siþ1
is the next posirotational movement Q iþ1;M
j
j
tion of the shark that is chosen based on the candidate solution
through the highest of value. The series of rotational and forward
movements is sustained until i attains imax . For the optimization
problem, the best individual achieved in the final stage is chosen
as the SSO solution.
Algorithm 1: Proposed J-SSO
Initialize shark population
Set the user defined parameters
Initialize stage counter as i ¼ 1
for i ¼ 1:imax
if rðof Þ < 0:5
Update velocity vector based on SSO using Eq. (12).
Update position based on SSO using Eq. (13).
Else
update solution based on JA using Eq. (16)
end if
end for i
Set i ¼ i þ 1
Choose the best position of shark at last stage that has the
highest of value
End
4.3.3. Conventional JA (Venkata Rao, 2016)
It is a powerful optimization algorithm for solving the unconstrained and constrained optimization issues. The conventional
JA has only one stage and it is easy to implement. This conventional
JA requires only common control parameters.
Let the number of design variables are selected as de, where
d ¼ 1; 2; . . . ; de at every iteration it. The number of candidate solutions is termed as cs where c ¼ 1; 2; . . . ; cs. Let the best candidate
and the worst candidate is termed as best and worst, respectively.
The best value of f ðsÞ and the worst value of f ðsÞ are achieved in the
entire candidate solutions. Furthermore, if the value of the variable
th
is denoted as Sd;c;it for d variable in the cth candidate through the
th
iteration it , then the value designed by the Eq. (16).
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Journal of King Saud University – Computer and Information Sciences 34 (2022) 2343–2358
D. Ahamad, S. Alam Hameed and M. Akhtar
The hybrid optimization concept is inspired from the combination
of various optimization rules or techniques. It has been proven that
the better results for the definite search problems. The hybrid optimization algorithms have been proven to obtain better convergence
by utilizing various optimization principles (Marsaline Beno et al.,
2014).
Fig. 3. Analysis on Degree of Modification of the proposed cyber security for cloud
data using (a) Dataset 1, (b) Dataset 2, (c) Dataset 3, (d) Dataset 4, and (e) Dataset 5.
5.5 Final objective function
5. Objective model derived for the developed cloud data cyber
security
The optimal key generation is achieved using the multiobjective function for the proposed cloud data cyber security
model by considering three parameters like degree of modification,
hiding ratio and information preservation ratio as given in Eq. (22).
5.1. Degree of modification
The degree of modification is described as ‘‘the degree of modification happened between the original dataset Ds and sanitized
data set Ds0 that is measured by finding the Euclidean distance
among Ds and Ds0 ”. The degree of modification is mathematically
formulated in Eq. (18).
F 1 ¼ Ds Ds0
Ob ¼ F 1 þ ð1 HRÞ þ ð1 PRÞ
ð22Þ
Here, the terms HR,PR, and F 1 represents the hiding ratio, the
information preservation ratio, and the degree of modification,
respectively.
ð18Þ
6. Experimental setup
5.2. Hiding ratio
6.1. Simulation setup
Hiding Ratio is defined as ‘‘the rate of sensitive items that are
correctly hided in Ds0 ”. This is used to find the index of value to
be hidden. The term D1 is considered as the difference between
the original data of the corresponding index, and the term D2 is
considered as the difference between the sanitized data of the corresponding index. The difference among D1 and D2 is given in Eq.
(19).
Ddiff ¼ abs ðD1 D2 Þ
The proposed cloud data cyber security model was implemented in MATLAB 2018a, and the analysis was carried out extensively for various security measures. The MATLAB has many
significant benefits like, easy to develop computational codes,
debug easily, and performs extensive data analysis and visualization. For handling the cloud data, MATLAB offers an unparalleled
environment to quickly develop and leverage various technologies
to build complete solutions. For conducting the experiments, the
maximum iteration was considered as 100 and the population size
was represented as 10. Five datasets were gathered for the experiments that were given in Table 2. Here, the performance of the
proposed model was compared over the conventional models
based on statistical analysis, convergence analysis, analysis on
KPA and CPA attacks, and key sensitivity analysis. The proposed
J-SSO algorithm was compared with the conventional optimization
algorithms like PSO (Bonyadi and Michalewicz, 2016), GWO
(Nirmala Sreedharan et al., 2018), JA (Venkata Rao, 2016), and
SSO (Abedinia et al., 2014). The optimal key generation is done
by obtaining a multi-objective function, which considers parameters such as degree of modification, hiding ratio, and information
preservation ratio. The degree of modification shows that the information loss happened in sanitized data when used along with the
original data. Hiding rate shows the effectiveness of the algorithm
in hiding the sensitive data and non-hiding the other data by preserving the information. Analysis on these metrics shows the ability of the algorithm in data sanitization and restoration with
optimal key generation, so that the algorithm confirms the security
in all types of cloud data.
ð19Þ
Consider, L1 as the length of the non-zero indexes of Ddiff and
the mathematical formula for hiding ratio is equated in Eq. (20).
HR ¼
L1
Ti
ð20Þ
In Eq. (20), the total number of data indexes that have to be
hided is termed as Ti. The hiding ratio should be maximized for
the best performance.
5.3. Information preservation ratio
Information preservation ratio ‘‘is defined as the rate of nonsensitive rules not hiding in Ds0 . The information preservation ratio
is the reciprocal of information loss”, which is given in Eq. (21).
PR ¼
L2
Tp
ð21Þ
In Eq. (21), the number of zero indexes is termed as L2 and the
total number of data indexes that have to be preserved is termed as
Tp. The preservation ratio should be maximized for the proposed
cyber security model.
6.2. Analysis on degree of modification
5.4. Solution encoding
The degree of modification is analyzed for various datasets
using diverse optimization algorithms via distance and iterations
as given in Fig. 4. As mentioned earlier, the degree of modification
is the Euclidean distance between the original and the sanitized
data. It shows the information loss happened in sanitized data
when correlated along with the original data. The degree of modification of the sanitized data should be minimum, which ensures
that there is no information loss during the sanitization approach.
The proposed J-SSO algorithm is producing minimal distance than
The proposed J-SSO algorithm is used for the optimization of
key for performing data sanitation and restoration. Based on the
size of the data or number of transactions, the length of key is
changed. The solution encoding of the key generation process is
given in Fig. 3. Here,ni ¼ 1; 2; :::; no, in which the no denotes the
length of the field or attribute. The bounding limit is given from
1 to 26 1. The key vectors are optimized using the J-SSO algorithm for generating the best solution.
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Journal of King Saud University – Computer and Information Sciences 34 (2022) 2343–2358
Fig. 4. Analysis on Preservation Ratio of the proposed cyber security for cloud data using (a) Dataset 1, (b) Dataset 2, (c) Dataset 3, (d) Dataset 4, and (e) Dataset 5.
enhanced than PSO, GWO, JA, and SSO, respectively at iteration
100. Similarly, for all the datasets the proposed J-SSO algorithm
has outperformed conventional algorithms regarding the preservation of relevant data in cloud.
conventional algorithms for all the iterations from 0 to 100. At iteration 100, J-SSO is 84% better than PSO and GWO, 83% better than
JA, and 80% better than SSO for dataset 1. For dataset 5, the proposed J-SSO algorithm is 60%, 5%, 78%, and 70% enhanced than
PSO, GWO, JA, and SSO, respectively at iteration 100. Therefore,
the proposed J-SSO algorithm is performing well than conventional
algorithms in terms of degree of modification for securing cloud
data.
6.4. Convergence analysis
The proposed J-SSO algorithm is analyzed based on the convergence for all the datasets with the conventional algorithms, which
is depicted in Fig. 6. For dataset 2, the proposed J-SSO is 95% better
than PSO and SSO, and 96% better than GWO, and JA. Similarly, for
dataset 5, it is 52%, 73%, and 66% enhanced than PSO, JA, and SSO,
respectively at iteration 100. Hence, the proposed algorithm shows
the better convergence in attaining the multi-objective function for
securing the cloud data.
6.3. Analysis on preservation ratio
The preservation ratio is analyzed for various datasets and they
are represented in Fig. 5. The enhanced preservation ratio of the
proposed J-SSO algorithm is observed for all the datasets and compared against conventional algorithms for various iterations. For
dataset 3, the proposed J-SSO algorithm is 87.5%, 50%, 90%, 2.5%
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Journal of King Saud University – Computer and Information Sciences 34 (2022) 2343–2358
Fig. 5. Convergence Analysis of the proposed cyber security model for cloud data using (a) Dataset 1, (b) Dataset 2, (c) Dataset 3, (d) Dataset 4, and (e) Dataset 5.
6.5. Comparative analysis over literature bio-inspired models
6.6. Analysis on KPA and CPA attacks
The proposed J-SSO algorithm is analyzed against the performance of the bio-inspired algorithms in the literature works for
all the datasets, and it is depicted in Fig. 7. For dataset 1, when
the iteration is 60, the proposed J-SSO is 72% better than BSWOA, 78% better than PSV-GWO, 83% better than GMGW and
73% better than OI-CSA. For dataset 4, when the iteration is 80,
the proposed J-SSO is 85% superior to BS-WOA, 88% superior to
PSV-GWO, 86% superior to GMGW and 83% superior to OI-CSA.
Similarly, for dataset 5, when the iteration is 80, the proposed JSSO is 97%, 96%, 98% and 95% enhanced than BS-WOA, PSV-GWO,
GMGW, and OI-CSA, respectively. Hence, it is confirmed that the
improved J-SSO is effective for securing the cloud data when compared with related works.
CPA is described as ‘‘an attack model for cryptanalysis which
presumes that the attacker can obtain the ciphertexts for arbitrary
plaintexts”. KPA is ‘‘an attack model for cryptanalysis where the
attacker has access to both the plaintext, and its encrypted version”. The effect on KPA and CPA attacks on various algorithms
for different datasets is given in Table 3. In this section, the correlation among the original and restored data is analyzed when the
CPA attack is done. Similarly, when the KPA attack is carried out,
then correlation among the original and restored data is analyzed.
During CPA and KPA attack, the correlation between the restored
data and original data becomes minimum. For data set 4, the correlation of the proposed J-SSO is 0.11%, 0.07%, 0.16, and 0.159
improved than PSO, GWO, JA, and SSO, respectively. For data set
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D. Ahamad, S. Alam Hameed and M. Akhtar
Journal of King Saud University – Computer and Information Sciences 34 (2022) 2343–2358
Fig. 6. Comparative analysis of the proposed model with related works for securing the cloud data using (a) Dataset 1, (b) Dataset 2, (c) Dataset 3, (d) Dataset 4, and (e)
Dataset 5.
1, the correlation of the proposed J-SSO is 0.2% superior to PSO,
GWO, JA, and SSO, respectively against CPA attack. Hence, the proposed algorithm has performed well and shown the enhanced efficiency against attacks than the conventional algorithms for
securing the cloud data.
ian of the J-SSO is 53%, 67%, 69%, and 66% superior to PSO, GWO, JA,
and SSO, respectively. The Standard deviation of the J-SSO is 47%,
6%, 31%, and 37% superior to PSO, GWO, JA, and SSO, respectively.
Thus, the proposed J-SSO has shown the enhanced performance
than the conventional algorithms.
6.7. Statistical analysis
6.8. Key sensitive analysis
In Table 4, the statistical analysis of various datasets is given.
Due to the stochastic nature of the algorithms, every algorithm is
required to be executed 5 times in terms of various measures like
best performance, worst performance, mean, median, standard
deviation. For dataset 2, the mean of the J-SSO is 42%, 57%, 61%,
and 60% superior to PSO, GWO, JA, and SSO, respectively. The med-
The performance of the extracted key is analyzed for various
percentage changes. Here, 10%, 20%, 30%, 50%, and 70% changes
are executed for evaluating the key sensitivity, which is given
in Table 5. The analysis provides the correlation between the
restored data and original data, which should be minimum for
changes in key. For dataset 4, at 70% changes in the key, it is
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Journal of King Saud University – Computer and Information Sciences 34 (2022) 2343–2358
D. Ahamad, S. Alam Hameed and M. Akhtar
Fig. 7. Proposed architecture of cloud data cyber security model.
Table 3
Effect on KPA and CPA attacks for various datasets in cloud.
Algorithms
Dataset 1
Dataset 2
Dataset 3
Dataset 4
Dataset 5
KPA attack
PSO (Bonyadi and Michalewicz, 2016)
GWO (Nirmala Sreedharan et al., 2018)
JA (Venkata Rao, 2016)
SSO (Abedinia et al., 2014)
Proposed J-SSO
0.99998
0.99992
0.99998
0.99999
0.99997
0.99999
0.99988
0.99959
0.99991
0.99759
0.99698
0.9983
0.99972
0.99979
0.99498
0.9993
0.99889
0.99972
0.99974
0.99815
0.99999
1
1
1
0.99799
CPA attack
PSO (Bonyadi and Michalewicz, 2016)
GWO (Nirmala Sreedharan et al., 2018)
JA (Venkata Rao, 2016)
SSO (Abedinia et al., 2014)
Proposed J-SSO
0.99999
0.99999
0.99999
0.99999
0.99799
1
0.99995
0.99995
0.99999
0.99795
0.99993
0.99986
0.99994
0.99993
0.99786
0.99995
0.99992
0.99997
0.99998
0.99992
1
1
1
1
0.998
6.9. Comparative analysis over MSCryptoNet (Kwabena et al., 2019)
71% and 67% PSO and GWO, respectively. For dataset 3, at 30%
changes in the key, it is 1.2%, 14%, 36%, and 1.09% PSO, GWO,
JA and SSO, respectively. Therefore, the proposed algorithm
has shown the better performance for the key sensitivity
analysis.
The comparative analysis of the proposed J-SSO for securing the
cloud data is compared with the MSCryptoNet and it is shown in
Table 6. For dataset 1, 2, 3, 4, and 5 the proposed J-SSO is 28.3%,
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D. Ahamad, S. Alam Hameed and M. Akhtar
Journal of King Saud University – Computer and Information Sciences 34 (2022) 2343–2358
Table 4
Statistical analysis on various datasets for proposed cloud data security.
Measures
Dataset 1
Best
Worst
Mean
Median
Standard deviation
PSO (Bonyadi and
Michalewicz, 2016)
GWO (Nirmala
Sreedharan et al., 2018)
JA (Venkata Rao,
2016)
SSO (Abedinia
et al., 2014)
Proposed J-SSO
12.917
15.632
14.47
15.237
1.2488
15.237
15.736
15.339
15.237
0.2223
9.7998
17.284
15.088
16
3.0657
11.608
19.22
17.187
18.447
3.1811
2.5918
8.4012
4.3883
3.6427
2.2985
Dataset 2
Best
Worst
Mean
Median
Standard deviation
0.94072
6.4628
4.2533
4.4961
2.024
4.4578
6.6864
5.7156
6.4628
1.1343
5.3572
7.0371
6.3388
6.7868
0.80731
5.4146
7.1643
6.2782
6.2809
0.77601
1.4369
3.7535
2.4564
2.0879
1.0656
Dataset 3
Best
Worst
Mean
Median
Standard deviation
1.1022
3.7543
2.1525
1.7947
1.1131
0.55068
0.57555
0.56557
0.56442
0.009693
0.61149
2.6038
1.2709
0.8682
0.80661
1.5107
2.3195
1.9279
1.9932
0.37934
0.52962
0.57451
0.5518
0.55068
0.015914
Dataset 4
Best
Worst
Mean
Median
Standard deviation
Best
3.5987
19.125
16.016
19.118
6.9414
3.5987
Dataset 5
Best
Worst
Mean
Median
Standard deviation
1.1191
6.5336
2.5551
1.499
2.2553
5.2544
30.186
11.864
6.6603
10.545
5.2544
4.3378
12.384
8.8163
10.159
3.3909
4.3378
0.67953
0.71822
0.69816
0.70192
0.01577
0.84149
3.5954
2.5193
2.5423
1.1269
4.2552
15.593
8.1586
6.0941
4.6798
4.2552
1.6645
2.7892
2.1414
2.1034
0.4968
3.6033
3.6473
3.6245
3.624
0.015563
3.6033
0.68512
0.72109
0.69289
0.68512
0.01581
Table 5
Key Sensitive Analysis on various Datasets for the proposed cloud data security.
Percentage
PSO (Bonyadi and
Michalewicz, 2016)
GWO (Nirmala
Sreedharan et al., 2018)
JA (Venkata Rao, 2016)
SSO (Abedinia
et al., 2014)
Proposed J-SSO
Dataset 1
10
20
30
50
70
0.99783
0.99941
0.98441
0.99364
0.99939
0.99987
0.99782
0.06289
0.40021
0.19573
0.7314
0.3182
0.99792
0.98905
0.99703
0.99372
0.99865
0.99731
0.64393
0.99515
0.95447
0.99372
0.9827
0.24289
0.43499
Dataset 2
10
20
30
50
70
0.99763
0.99563
0.99444
0.99229
0.063081
0.99916
0.3111
0.18802
0.38059
0.98245
0.99251
0.97842
0.022587
0.95736
0.48956
0.75247
0.9965
0.97948
0.9822
0.074526
0.99969
0.98357
0.9987
0.73574
0.15383
Dataset 3
10
20
30
50
70
0.78208
0.88274
0.85458
0.52012
0.81045
0.89968
0.92424
0.98892
0.80295
0.29649
0.90693
0.98576
0.61996
0.83413
0.5028
0.94484
0.43935
0.86063
0.71961
0.85674
0.00604
0.99058
0.84422
0.52048
0.39555
Dataset 4
10
20
30
50
70
0.50446
0.9187
0.65577
0.90332
0.60003
0.94688
0.6714
0.87048
0.42983
0.53903
0.62869
0.08622
0.40617
0.60069
0.56664
0.94629
0.79409
0.52633
0.77527
0.58136
0.66323
0.66716
0.66457
0.22032
0.17283
Dataset 5
10
20
30
50
70
0.99918
1
0.99997
0.99999
0.99001
0.9987
1
0.99999
0.99998
0.99998
1
0.99998
0.99999
1
0.99968
0.99999
1
0.99949
0.99995
0.99993
2356
0.9997
0.99998
0.9996
0.99207
0.99991
Journal of King Saud University – Computer and Information Sciences 34 (2022) 2343–2358
D. Ahamad, S. Alam Hameed and M. Akhtar
tage of GMGW is that the accuracy of restoration seems to be very
low. Moreover, the techniques OI-CSA and BS-WOA suffer from low
convergence behavior in maintaining the privacy of every database. Moreover, the proposed J-SSO keeps its ability in solving
the multi-objective privacy preservation problem with fast convergence rate and attained better results while dealing with different
attacks and varied keys, and is proved to be computationally efficient that other methods. From the analysis, for dataset 5, proposed J-SSO is 52%, 73%, and 66% enhanced than PSO, JA, and
SSO, respectively at iteration 100.Therefore, the proposed algorithm has shown better performance through statistical analysis,
convergence analysis, analysis on KPA and CPA attacks, and key
sensitivity analysis over the conventional algorithms. Cloud security application is a series of identified processes, controls, and
technology managing all information exchanges that happen in
collaborative cloud environments like Microsoft Office 365, Google
G Suite, and Slack. Moreover, the cloud computing paradigm plays
a vital role in many applications like, like resource allocation,
scheduling, energy management, virtualization, and security, and
these areas are intertwined with many optimization problems like
the proposed model.
Table 6
Comparative analysis of the proposed model over MSCryptoNet for securing the cloud
data.
Dataset
MSCryptoNet
(Kwabena et al., 2019)
Proposed
J-SSO
Air quality
Concrete data
Heart disease
Super conductivity
Whole sale customer data
0.52844
0.54957
0.75202
0.54468
0.587
0.67844
0.74957
0.90202
0.74468
0.837
Table 7
Computational time of the proposed and existing methods for securing the cloud data.
Methods
Computational time(sec)
PSO (Bonyadi and Michalewicz, 2016)
GWO (Nirmala Sreedharan et al., 2018)
JA (Venkata Rao, 2016)
SSO (Abedinia et al., 2014)
BS-WOA (Thanga Revathi et al., 2019)
PSV-GWO (Mandala, 2019)
GMGW (Annie Alphonsa and Amudhavalli, 2018)
OI-CSA (Shailaja and Guru Rao, 2019)
MSCryptoNet (Kwabena et al., 2019)
Proposed J-SSO
142.38
159.11
159.24
160.26
128.85
133.13
133.33
134.78
272.38
129.47
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared
to influence the work reported in this paper.
36.3%, 19.9%, 36.7%, and 42.5% superior to MSCryptoNet. Hence, it
is confirmed that the improved J-SSO is effective for securing the
cloud data when compared with MSCryptoNet.
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