IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY (IJCSMT )

E-ISSN 2545-5699
P-ISSN 2695-1924
VOL. 11 NO. 2 2025
DOI: 10.56201/ijcsmt.v11.no2.2025.pg106.126

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Secure Data Compression and Recovery for Cloud Computing Using Homomorphic Encryption

Manga, O Sarjiyus, RB Jean

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Abstract

Finding a balance between effective data compression and strong security is still a major challenge as data processing and storage move more and more to cloud services. Conventional compression methods maximize storage capacity but sometimes overlook security, leaving private information vulnerable to attacks. This paper investigates how text compression and Fully Homomorphic Encryption (FHE) can be combined in safe cloud computing settings. The goal of the project is to provide a framework that improves data recovery methods for compressed and encrypted data in the cloud, as well as a novel model that achieves optimal lossless compression while upholding robust data security. The results show that larger files have lower compression ratios and less redundancy; for example, a 10 KB file compresses 50% of its size, while a 1000 KB file only reduces by 29%. This illustrates how redundancy-based compression loses effectiveness when dealing with bigger datasets. The work supports earlier findings on the computational cost of FHE by highlighting its large computational overhead, where encryption and decryption durations dramatically increase with text size. Hybrid encryption models that combine symmetric and asymmetric encryption may offer a better balanced approach to efficiency and security, according to a comparison of FHE and conventional cryptographic compression techniques. Furthermore, the observed drop in compression ratios from 0.6 for 1 KB files to 0.29 for 1000 KB files is consistent with entropy-based encoding methods such as Huffman coding and Lempel-Ziv compression. The paper highlights that because of its high computational cost, standalone FHE is still not feasible for real-time secure applications. Future research should concentrate on improving FHE schemes, investigating parallelized implementations, and creating hybrid encryption models to lessen performance constraints in secure cloud computing, even though FHE

keywords:

Secure, Data Compression, Recovery, Cloud Computing Homomorphic Encryption

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