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SoK: Fully Homomorphic Encryption Accelerators
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DOI：10.1145/3676955.png)
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Fully Homomorphic Encryption (FHE) is a key technology enabling privacy-preserving computing. However, the fundamental challenge of FHE is its inefficiency, due primarily to the underlying polynomial computations with high computation complexity and extremely time-consuming ciphertext maintenance operations. To tackle this challenge, various FHE accelerators have recently been proposed by both research and industrial communities. This article takes the first initiative to conduct a systematic study on the 14 FHE accelerators: cuHE/cuFHE, nuFHE, HEAT, HEAX, HEXL, HEXL-FPGA, 100x, F1, CraterLake, BTS, ARK, Poseidon, FAB, and TensorFHE. We first make our observations on the evolution trajectory of these existing FHE accelerators to establish a qualitative connection between them. Then, we perform testbed evaluations of representative open-source FHE accelerators to provide a quantitative comparison on them. Finally, with the insights learned from both qualitative and quantitative studies, we discuss potential directions to inform the future design and implementation for FHE accelerators.
Keyword:
Fully homomorphic encryption
accelerator
期刊
IF:
28
论文数:
2.5K
被引数:
3.5W
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Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
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