ISSN 2996-8223
International Journal of Computer Science and Technology | Vol. 4, No. 2, February 2023 | pp. 1–8
DOI: 10.46882/2023/IJCST/000217
Article Type: Review Paper
Title: Homomorphic Encryption Pipelines for Secure Cloud Analytics: A Performance Review
Names of Authors: Oliver Smith¹, Sophia Rossi²
Authors’ Affiliations: ¹Department of Cryptographic Research, Manchester Advanced Science University, Manchester, UK; ²Data Protection Systems Division, CyberGuard Labs, Milan, Italy
Abstract: Fully Homomorphic Encryption (FHE) offers a robust privacy framework by enabling mathematical operations directly on encrypted data payloads. This capability allows cloud servers to process sensitive data fields without decrypting the underlying source records. This review paper analyzes the evolution of FHE implementation frameworks over the past decade. We contrast lattice-based mathematical structures including BGV, BFV, and CKKS schemes across cloud environments. The analysis tracks key performance metrics including key sizing requirements, processing overhead factors, and noise growth rates. We compiled empirical benchmarks from 32 peer-reviewed implementations across standard business operations. The evaluation indicates that while CKKS optimizes floating-point operations for machine learning workloads, processing demands remain up to 100x slower than unencrypted workflows. The review outlines hardware acceleration strategies using modern FPGA and ASIC chipsets to mitigate these performance bottlenecks. Finally, we provide a structured configuration guide to help developers choose FHE frameworks based on specific operational requirements.
Keywords: Fully Homomorphic Encryption, Cloud Computing, Data Privacy, Lattice-Based Cryptography, Performance Benchmarks, Hardware Acceleration
Manuscript Timeline: Received: June 05, 2022; Revised: July 12, 2022; Accepted: August 03, 2022; Published: February 11, 2023
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