A survey of threshold homomorphic encryption application in privacy preserving computing

doi:10.12267/j.issn.2096-5931.2021.07.012

Abstract

Abstract: As a solution of secure data cooperation between multi-parties, privacy preserving computing is attracting much attention in recent years. Homomorphic Encryption (HE) is one of the most important technologies in privacy preserving computing. However, most HE algorithms only have one pair of secret key and public key, and the computations between ciphertexts encrypted by different public keys are not supported. Such only-one-secret-key character brings lots of trouble to the HE application in privacy preserving computation. Recently, a threshold HE solution attracts much attention, and it supports more than one secret key. This paper introduces the threshold HE method, then show its application in privacy preserving computation.

Key words: privacy preserving computing, application of privacy preserving computing, homomorphic encryption, threshold homomorphic encryption, multikey homomorphic encryption

CHAI Di. A survey of threshold homomorphic encryption application in privacy preserving computing[J]. Information and Communications Technology and Policy, 2021, 47(7): 82-86.

Add to citation manager EndNote|Ris|BibTeX

URL:

http://ictp.caict.ac.cn/EN/10.12267/j.issn.2096-5931.2021.07.012

http://ictp.caict.ac.cn/EN/Y2021/V47/I7/82

References 12

[1]	Sav S, Pyrgelis A, Troncoso-Pastoriza J R, et al. POSEIDON: privacy-preserving federated neural network
	learning[J], arXiv Preprint, arXive: 2009. 00349,2020.
[2]	Chen H, Dai W, Kim M, et al. Efficient multi-key homomorphic encryption with packed ciphertexts with application to oblivious neural network inference [C] / / the 2019 ACM SIGSAC Conference, 2019.
[3]	Ping L , Li J, Huang Z, et al. Multi-key privacypreserving deep learning in cloud computing[J]. Future
	Generation Computer Systems, 2017,74(9):76-85.
[4]	Mouchet C. Multiparty homomorphic encryption: from theory to practice [ C ]. IACR Cryptol ePrint Arch, 2020.
[5]	Lopez-Alt, Adriana, Eran Tromer, et al. On-the-fly multiparty computation on the cloud via multikey fully
	homomorphic encryption[C]. Proceedings of the Fortyfourth Annual ACM Symposium on Theory of Computing, 2012.
[6]	Asharov, Gilad. Multiparty computation with low communication, computation and interaction via threshold FHE[C]. Annual International Conference on the Theory and Applications of Cryptographic Techniques, 2012.
[7 ]	Cramer, Ronald, Ivan Damgard, et al. Multiparty computation from threshold homomorphic encryption
[ C ]	International Conference on the Theory and Applications of Cryptographic Techniques, 2001.
[8]	Wu Y, Cai S, Xiao X, et al. Privacy preserving vertical federated learning for tree-based models [ J ], arXiv Preprint, arXive: 2008. 06170,2020.
[9]	Ma J, Naas S A, Sigg S, et al. Privacy-preserving federated learning based on multi-key homomorphic encryption [ J], arXiv Preprint, arXive: 2104. 06824,2021.
[10]	Karabat C, Kiraz M S, Erdogan H, et al. THRIVE: threshold homomorphic encryption based secure and
	privacy preserving bIometric VErification system [ J ], arXiv Preprint, arXive: 1409. 8212,2014.
[11]	Chen H, Chillotti I, Song Y. Multi-key homomorphic encryption from TFHE[C]. International Conference on
	the Theory and Application of Cryptology and Information Security, 2019.
[12]	Hazay C, Mikkelsen G L, Rabin T, et al. Efficient RSA key generation and threshold paillier in the two-party setting [ J ]. Journal of Cryptology, 2019, 32 ( 2):265-323.

[1]	ZHAO Jingwu, ZHOU Ruijue . Privacy preserving computing: the construction of synchronization rules for data flow and data security [J]. Information and Communications Technology and Policy, 2021, 47(7): 53-.
[2]	LIANG Deng. The legal boundaries of targeting advertising applications for privacy preserving computing [J]. Information and Communications Technology and Policy, 2021, 47(7): 66-75.
[3]	YAN Shu, LYU Ailin. Overview of the development of privacy preserving computing [J]. Information and Communications Technology and Policy, 2021, 47(6): 1-11.
[4]	YUAN Bo, WANG Siyuan. Privacy preserving computing product evaluation system [J]. Information and Communications Technology and Policy, 2021, 47(6): 12-18.
[5]	XIA Jiajun, LU Ying, ZHANG Ziyang, ZHANG Yuting, ZHANG Jiachen. Research on vertical federated learning based on secret sharing and homomorphic encryption [J]. Information and Communications Technology and Policy, 2021, 47(6): 19-26.
[6]	FU Fangcheng, HOU Chen, CHENG Yong, TAO Yangyu. Key technology and innovation of privacy preserving computing [J]. Information and Communications Technology and Policy, 2021, 47(6): 27-37.
[7]	XU Qian, ZHANG Qing, YU Bo, YU Wenqing, HE Wei. Middleware and blockchain based interconnecting system of heterogeneous privacy preserving computing platforms [J]. Information and Communications Technology and Policy, 2021, 47(6): 38-49.
[8]	HUANG Cuiting, ZHANG Fan, SUN Xiaochao, BIAN Yang. A survey of Private Set Intersection technology and finance practice [J]. Information and Communications Technology and Policy, 2021, 47(6): 50-.
[9]	QIANG Feng, XUE Yushan, XIANG Mei. Privacy preserving computing in the financial field [J]. Information and Communications Technology and Policy, 2021, 47(6): 57-62.
[10]	FENG Haoming, CHEN Hang, LIU Yanping. Ablockchain asset trading system based on homomorphic encryption and ring signature [J]. Information and Communications Technology and Policy, 2020, 46(1): 64-68.