A Survey on Software-hardware Acceleration for Fully Homomorphic Encryption

被引:0
|
作者
Bian S. [1 ]
Mao R. [1 ]
Zhu Y. [1 ]
Fu Y. [1 ]
Zhang Z. [1 ]
Ding L. [2 ]
Zhang J. [2 ]
Zhang B. [3 ]
Chen Y. [3 ]
Dong J. [3 ]
Guan Z. [1 ]
机构
[1] School of Cyber Science and Technology, Beihang University, Beijing
[2] College of Semiconductors (College of Integrated Circuits), Hunan University, Changsha
[3] The Beijing Academy of Blockchain and Edge Computing, Beijing
基金
中国国家自然科学基金;
关键词
Cryptographic hardware acceleration; Fully Homomorphic Encryption(FHE); Homomorphic algorithm;
D O I
10.11999/JEIT230448
中图分类号
学科分类号
摘要
Fully Homomorphic Encryption (FHE) is a multi-party secure computation protocol characterized by its high computational complexity and low interaction requirements. Although there is no need for multiple rounds of interactions and extensive communications between computing participants in protocols based on FHE, the processing time of encrypted data is typically 103 to 106 times of that of plaintext computing, and thus significantly hinders the practical deployment of such protocols. In particular, the large-scale darallel cryptographic operations and the cost of data movement for the ciphertext and key data needed in the operations become the dominating performance bottlenecks. The topic of accelerating FHE in both the software and the hardware layers is discussed in this paper. By systematically categorizing and organizing existing literatures, a survey on the current status and outlook of the research on FHE is presented. © 2024 Science Press. All rights reserved.
引用
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页码:1790 / 1805
页数:15
相关论文
共 80 条
  • [41] MICCIANCIO D, SORRELL J., Ring packing and amortized FHEW bootstrapping, Cryptology ePrint Archive, 2018, (2018)
  • [42] GUIMARAES A, PEREIRA H V L, VAN LEEUWEN B., Amortized bootstrapping revisited: Simpler, asymptotically-faster, implemented, Cryptology ePrint Archive 2023/014, (2023)
  • [43] LIU Fenghao, WANG Han, Batch bootstrapping I: A new framework for SIMD bootstrapping in polynomial modulus[C], The 42nd Annual International Conference on the Theory and Applications of Cryptographic Techniques, pp. 321-352, (2023)
  • [44] BOURA C, GAMA N, GEORGIEVA M, Et al., Simulating homomorphic evaluation of deep learning predictions[C], The 3rd International Symposium on Cyber Security Cryptography and Machine Learning, pp. 212-230, (2019)
  • [45] CHILLOTTI I, JOYE M, PAILLIER P., Programmable bootstrapping enables efficient homomorphic inference of deep neural networks[C], The 5th International Symposium on Cyber Security Cryptography and Machine Learning, pp. 1-19, (2021)
  • [46] CLET P E, ZUBER M, BOUDGUIGA A, Et al., Putting up the swiss army knife of homomorphic calculations by means of TFHE functional bootstrapping, Cryptology ePrint Archive, 2022, (2022)
  • [47] KLUCZNIAK K, SCHILD L., FDFB: Full domain functional bootstrapping towards practical fully homomorphic encryption[J], IACR Transactions on Cryptographic Hardware and Embedded Systems, 2023, 1, pp. 501-537, (2022)
  • [48] YANG Zhaomin, XIE Xiang, SHEN Huajie, Et al., TOTA: Fully homomorphic encryption with smaller parameters and stronger security, Cryptology ePrint Archive, (2021)
  • [49] CARPOV S, GAMA N, GEORGIEVA M, Et al., Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption, BMC Medical Genomics, 13, 7, (2020)
  • [50] GUIMARAES A, BORIN E, ARANHA D F., Revisiting the functional bootstrap in TFHE[J], IACR Transactions on Cryptographic Hardware and Embedded Systems, 2021, 2, pp. 229-253, (2021)