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author:

Zhang, Junxue (Zhang, Junxue.) [1] | Cheng, Xiaodian (Cheng, Xiaodian.) [2] | Yang, Liu (Yang, Liu.) [3] | Hu, Jinbin (Hu, Jinbin.) [4] | Liu, Ximeng (Liu, Ximeng.) [5] | Chen, Kai (Chen, Kai.) [6]

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Abstract:

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, 100×, 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. © 2024 held by the owner/author(s).

Keyword:

Colliding beam accelerators Privacy-preserving techniques

Community:

  • [ 1 ] [Zhang, Junxue]The Hong Kong University of Science and Technology, Hong Kong, Hong Kong
  • [ 2 ] [Zhang, Junxue]Clustar, Hong Kong, Hong Kong
  • [ 3 ] [Cheng, Xiaodian]The Hong Kong University of Science and Technology, Hong Kong, Hong Kong
  • [ 4 ] [Yang, Liu]The Hong Kong University of Science and Technology, Hong Kong, Hong Kong
  • [ 5 ] [Yang, Liu]Clustar, Hong Kong, Hong Kong
  • [ 6 ] [Hu, Jinbin]The Hong Kong University of Science and Technology, Hong Kong, Hong Kong
  • [ 7 ] [Liu, Ximeng]Fuzhou University, Fuzhou, China
  • [ 8 ] [Chen, Kai]The Hong Kong University of Science and Technology, Hong Kong, Hong Kong

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Source :

ACM Computing Surveys

ISSN: 0360-0300

Year: 2024

Issue: 12

Volume: 56

2 3 . 8 0 0

JCR@2023

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ESI Highly Cited Papers on the List: 0 Unfold All

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30 Days PV: 0

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