A Multi-Layer Evaluation Framework for Encrypted Data Processing in Cloud Environments Using Fully Homomorphic Re-encryption
Keywords:
Fully Homomorphic Encryption, Cloud Com- puting, Multi-Layer Framework, Performance Evaluation, Secu- rity Analysis, Homomorphic Re-encryptionAbstract
Fully Homomorphic Encryption (FHE) enables se- cure computation on encrypted data in cloud environments, but practical deployment faces significant challenges in performance, security evaluation, and system integration. This paper presents a comprehensive multi-layer evaluation framework for encrypted data processing using FHE schemes with homomorphic re- encryption capabilities. Our framework systematically addresses four critical layers: application, encryption, storage, and compu- tation. We provide detailed comparative analysis of major FHE schemes (BFV, BGV, CKKS, TFHE) across performance metrics, security criteria, and implementation considerations. Experimen- tal results demonstrate that GPU-accelerated implementations achieve up to 252× speedup over CPU baselines, while privacy- aware compilers reduce latency by 4.92× through intelligent edge- cloud partitioning. Our security analysis framework incorpo- rates cryptographic hardness evaluation, multi-key support, and verifiable computation mechanisms. The proposed framework provides actionable guidelines for practitioners deploying FHE- based systems in production cloud environments.
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References
Hamza, “A Quantum-Resistant FHE Framework for Privacy- Preserving Image Processing in the Cloud,” Algorithms, 2025. DOI: 10.3390/a18080480
Saker et al., “Comparative analysis of homomorphic encryption schemes for encrypted image processing in OpenStack using TenSEAL,” Az Eszterha´zy Ka´roly Tana´rke´pzo˝ Fo˝iskola tudoma´nyos ko¨zleme´nyei, 2025. DOI: 10.33039/ami.2025.09.001
Fan et al., “Cloud-Assisted Private Set Intersection via Multi-Key Fully Homomorphic Encryption,” Mathematics, vol. 11, no. 8, 2023. DOI: 10.3390/math11081784
Kenhove et al., “MOZAIK: A Privacy-Preserving Analytics Platform for IoT Data Using MPC and FHE,” 2026.
Cai et al., “SecFed: A Secure and Efficient Federated Learning Based on Multi-Key Homomorphic Encryption,” IEEE Transactions on Depend- able and Secure Computing, 2023. DOI: 10.1109/tdsc.2023.3336977
Ameur et al., “Handling security issues by using homomorphic encryp- tion in multi-cloud environment,” Procedia Computer Science, 2023. DOI: 10.1016/j.procs.2023.03.050
Kim et al., “HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Networks,” arXiv.org, 2023. DOI: 10.48550/arXiv.2302.02407
Tsuji et al., “Comparison of FHE Schemes and Libraries for Efficient Cryptographic Processing,” in Proc. ICNC, 2024. DOI: 10.1109/icnc59896.2024.10556382
Agullo´-Domingo et al., “FIDESlib: A Fully-Fledged Open-Source FHE Library for Efficient CKKS on GPUs,” in Proc. ISPASS, 2025. DOI: 10.1109/ispass64960.2025.00045
Liao et al., “A multikey fully homomorphic encryption privacy pro- tection protocol based on blockchain for edge computing system,” Concurrency and Computation: Practice and Experience, 2022. DOI: 10.1002/cpe.7539
AUTHOR ID et al., “PEEV: Parse Encrypt Execute Verify - A Verifiable FHE Framework,” IEEE Access, 2024. DOI: 10.1109/ac- cess.2024.3424420
Xiao et al., “Privacy Protection Anomaly Detection in Smart Grids Based on Combined PHE and TFHE Homomorphic Encryption,” Electronics, vol. 14, no. 12, 2025. DOI: 10.3390/electronics14122386
Zheng et al., “Accurate and Efficient Privacy-Preserving Feature Ex- traction on Encrypted Images,” IEEE Transactions on Dependable and Secure Computing, 2025. DOI: 10.1109/tdsc.2024.3524121
Kim et al., “Privacy Set: Privacy Authority-Aware Compiler for Homo- morphic Encryption on Edge-Cloud System,” IEEE Internet of Things Journal, 2024. DOI: 10.1109/jiot.2024.3437356
Li et al., “Privacy preserving via multi-key homomorphic encryption in cloud computing,” Journal of information security and applications, 2023. DOI: 10.1016/j.jisa.2023.103463.
Wang et al., “HE-Booster: An Efficient Polynomial Arithmetic Acceleration on GPUs for Fully Homomorphic Encryption,” IEEE Transactions on Parallel and Distributed Systems, 2023. DOI: 10.1109/TPDS.2022.3228628.
Olaymi, “Performance and security analysis of fully homomorphic encryption in cloud-based healthcare blockchain,” 2023.
Ilyenko et al., “Practical Aspects of Using Fully Homomorphic Encryption Systems to Protect Cloud Computing,” 2023.
Ali, “Towards Practical Homomorphic Encryption: A Review of Implementations, Side-Channel Threats, and the OHHE Framework Proposal,”2023.
Yadavalli et al., “Homomorphic Encryption Methods Applied to Cloud Computing: A Practical Architecture for Elastic, Verifiable Confidential Compute,” 2023.
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