CuLOA-based Data Encryption with Tuned Key for Privacy Preservation in the Cloud
Received: 26 July 2024 | Revised: 28 August 2024 | Accepted: 29 November 2024 | Online: 2 February 2025
Corresponding author: Rajkumar Patil
Abstract
Preservation of data privacy in cloud computing involves securing sensitive data during analysis and storage. Conventional approaches often use techniques such as encryption and differential privacy, but they can be computationally intensive and may still risk data leakage through indirect inferences. These limitations necessitate advanced methods to balance efficiency and robust privacy. To address this, this study proposes a novel approach using DL-based fine-tuned keys for encrypting the data, aimed at preserving data privacy in the cloud through the Coati Lyrebird Optimization Algorithm (CuLOA) approach. Initially, sensitive data is randomly chosen from the database. Then, the optimal key is derived using the CuLOA approach. This key, along with the sensitive data is input into the SqueezeNet model, which generates a fine-tuned optimal key. Subsequently, the sensitive data are encrypted and stored in cloud storage. Finally, the encrypted data and the optimally tuned key are employed in the data decryption process to recover the original. A comparative experimental analysis showed that the proposed CuLOA approach was better than previous schemes.
Keywords:
privacy preservation in the cloud, data encryption, SqueezeNet, data decryption, CuLOADownloads
References
J. S. Lee and S. P. Jun, "Privacy-preserving data mining for open government data from heterogeneous sources," Government Information Quarterly, vol. 38, no. 1, Jan. 2021, Art. no. 101544.
K. Dave and C. Chand, "Privacy-Preserving in Data Mining using Anonymity Algorithm for Relational Data," International Journal of Science and Research (IJSR), vol. 5, no. 3, pp. 1694–1698, Mar. 2016.
D. Ahamad, S. Alam Hameed, and M. Akhtar, "A multi-objective privacy preservation model for cloud security using hybrid Jaya-based shark smell optimization," Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 6, Part A, pp. 2343–2358, Jun. 2022.
M. Rafiei and W. M. P. van der Aalst, "Group-based privacy preservation techniques for process mining," Data & Knowledge Engineering, vol. 134, Jul. 2021, Art. no. 101908.
J. A. I. S. Masood, M. Jeyaselvi, N. Senthamarai, S. Koteswari, M. Sathya, and N. S. K. Chakravarthy, "Privacy preservation in wireless sensor network using energy efficient multipath routing for healthcare data," Measurement: Sensors, vol. 29, Oct. 2023, Art. no. 100867.
G. Verma, "Blockchain-based privacy preservation framework for healthcare data in cloud environment," Journal of Experimental & Theoretical Artificial Intelligence, vol. 36, no. 1, pp. 147–160, Jan. 2024.
P. Jia, J. Zhang, B. Zhao, H. Li, and X. Liu, "Privacy-preserving association rule mining via multi-key fully homomorphic encryption," Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 2, pp. 641–650, Feb. 2023.
R. R. Nikam and R. Shahapurkar, "Data Privacy Preservation and Security Approaches for Sensitive Data in Big Data," in Advances in Parallel Computing, M. Rajesh, K. Vengatesan, M. Gnanasekar, Sitharthan.R, A. B. Pawar, P. N. Kalvadekar, and P. Saiprasad, Eds. IOS Press, 2021.
B. Li and K. He, "Local generalization and bucketization technique for personalized privacy preservation," Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 1, pp. 393–404, Jan. 2023.
J. M. Parra-Ullauri et al., "kubeFlower: A privacy-preserving framework for Kubernetes-based federated learning in cloud–edge environments," Future Generation Computer Systems, vol. 157, pp. 558–572, Aug. 2024.
M. Kumar et al., "A smart privacy preserving framework for industrial IoT using hybrid meta-heuristic algorithm," Scientific Reports, vol. 13, no. 1, Apr. 2023, Art. no. 5372.
M. Dehghani, G. Bektemyssova, Z. Montazeri, G. Shaikemelev, O. P. Malik, and G. Dhiman, "Lyrebird Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems," Biomimetics, vol. 8, no. 6, Oct. 2023, Art. no. 507.
M. Dehghani, Z. Montazeri, E. Trojovská, and P. Trojovský, "Coati Optimization Algorithm: A new bio-inspired metaheuristic algorithm for solving optimization problems," Knowledge-Based Systems, vol. 259, Jan. 2023, Art. no. 110011.
L. S. Bernardo, R. Damaševičius, S. H. Ling, V. H. C. de Albuquerque, and J. M. R. S. Tavares, "Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data," Biomedicines, vol. 10, no. 11, Nov. 2022, Art. no. 2746.
A. Janosi, W. Steinbrunn, M. Pfisterer, and R. Detrano, "Heart Disease." UCI Machine Learning Repository, 1989.
Downloads
How to Cite
License
Copyright (c) 2024 Rajkumar Patil, Gottumukkala HimaBindu
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
