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Mhaske and Kumar

(BT). BT is an effective distributed ledger system for The Siberian Tiger Optimization (STO) algorithm,
effectively recording transactions between two parties. proposed by Trojovský et al., was motivated by the
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Every transaction is stored in a “block,” and these blocks predatory tactics of Siberian tigers. This algorithm
are then joined together using encryption to create a demonstrated its ability to handle difficult optimization
blockchain. 14-16 As a decentralized transaction system, tasks by performing exceptionally well in engineering
BT can also facilitate data management. Secure network optimization problems. The combination of these
transactions are carried out through BT, 17,18 which does naturally inspired methods demonstrates the continuous
not rely on a centralized authority. This ensures data progress in evolutionary computing and swarm
integrity, security, and transparency without intrusion intelligence.
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from any external organization. This is one of the key In 2021, Verma presented a unique blockchain
reasons behind the growing interest in BT, 19-21 which system to secure health records in the cloud. This
in turn creates research opportunities across various technology ensured the authentication and integrity of
fields. 16,17 In addition, homomorphic encryption enables medical information. To achieve this, they employed an
computations to be performed on encrypted data without enhanced Blowfish model that ensured authentication
the need for decryption, enabling secure processing of features were used to install blockchain with the best
encrypted health-care data 22,23 while preserving privacy encryption. In addition, a novel method known as the
and facilitating analysis and insights. Elephant Herding Optimization with Opposition-based
Below are the contributions of the proposed privacy Learning (EHO-OBL) was used to generate optimal
preservation (PP) model for EHR using BT: keys. Thus, the created technique preserved the integrity
(i) A new PP model is proposed, which introduces an of the data, and the superiority of the proposed approach
improved association rule (ASR) mining (ARM) was demonstrated through various performance metrics.
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method for mining rules. This avoids data leaks and In 2023, Irshad et al. proposed data restoration
addresses the complexity of interpreting results. and sanitization procedures to generate keys from the
In addition, it can uncover complicated and subtle collected data, creating an objective function for the
associations in the data and manage data variations information preservation ratio (IPR), modification
over time degree (MD), and hiding failure rate (HFR). To ensure
(ii) The model introduces the Siberian Tiger Integrated robust security when transferring health-care data to the
Tuna Swarm algorithm (STI-TSA) optimization for cloud, they employed the bee-foraging learning particle
optimal key generation by including the concepts swarm optimization (BFL-PSO) method to identify the
of Secretary Bird Optimization (SBO) and the Tuna optimal key.
Swarm algorithm. The STI-TSA optimization could Large datasets have necessitated the development of
attain faster convergence and create high-quality effective data mining and privacy-preserving methods.
solutions. A reference point for assessing machine learning models
in health-care analytics is the University of California
The review of PP with BT is presented in Section 2. An Irvine Heart Disease dataset. An enhanced ARM
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overview of the proposed work is provided in Section 3. approach was proposed by Zhao et al. to improve
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Improved ARM and STI-TSA are explained in Sections the effectiveness of pattern finding in huge datasets,
4 and 5, respectively. Data restoration is explained in showing notable gains in accuracy and processing time.
Section 6. The results and conclusions are presented in To improve data security in cloud contexts, Ahamad
Sections 7 and 8. et al. presented a multi-objective PP model that
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uses a hybrid Jaya-based Shark Smell Optimization
2. Literature review technique. Furthermore, a modified Apriori approach
was introduced by Baffour et al. to speed up and
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Bio-inspired metaheuristic algorithms have recently increase the accuracy of frequent itemset creation. These
gained significant attention as effective tools for studies collectively contribute to the advancement of
resolving challenging optimization issues. The Tuna data mining, security, and optimization techniques in
Swarm Optimization (TSO), introduced by Xie handling large-scale data.
et al., improves global optimization performance by A new scheme that employed medical experts to
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imitating the hunting and foraging habits of tuna fish. monitor patient data and provide extra units was proposed
Their research showed how effective TSO is at solving by Saraswat et al. in 2023. Several experiments were
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a range of benchmark and practical optimization issues. conducted to evaluate the performance of the suggested

Volume 22 Issue 1 (2025) 150 doi: 10.36922/AJWEP025040017