Practical Solutions in Fully Homomorphic Encryption -- A Survey Analyzing Existing Acceleration Methods
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Abstract (arXiv)
Fully homomorphic encryption (FHE) has experienced significant development and continuous breakthroughs in theory, enabling its widespread application in various fields, like outsourcing computation and secure multi-party computing, in order to preserve privacy. Nonetheless, the application of FHE is constrained by its substantial computing overhead and storage cost. Researchers have proposed practical acceleration solutions to address these issues. This paper aims to provide a comprehensive survey for systematically comparing and analyzing the strengths and weaknesses of FHE acceleration schemes, which is currently lacking in the literature. The relevant researches conducted between 2019 and 2022 are investigated. We first provide a comprehensive summary of the latest research findings on accelerating FHE, aiming to offer valuable insights for researchers interested in FHE acceleration. Secondly, we classify existing acceleration schemes from algorithmic and hardware perspectives. We also propose evaluation metrics and conduct a detailed comparison of various methods. Finally, our study presents the future research directions of FHE acceleration, and also offers both guidance and support for practical application and theoretical research in this field.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Mar 2023.
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Citing papers per year: 2024: 2; 2025: 1; 2026: 2.
5 papers.
- 2024MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryptioncited by 7
- 2024Parameter-Efficient Fine-Tuning via Circular Convolutioncited by 4
- 2026Manipulation-Proof Oblivious Audits against Deceptive Model Providerscited by 1
- 2025Securing Transfer-Learned Networks with Reverse Homomorphic Encryptioncited by 0
- 2026Classification Under Local Differential Privacy with Model Reversal and Model Averagingcited by 0
- 2026Manipulation-Proof Oblivious Audits against Deceptive Model Providers5 Aug 2026
- 2026Classification Under Local Differential Privacy with Model Reversal and Model Averaging5 Feb 2026
- 2025Securing Transfer-Learned Networks with Reverse Homomorphic Encryption20 May 2025
- 2024MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption30 Nov 2024
- 2024Parameter-Efficient Fine-Tuning via Circular Convolution27 Jul 2024