基于多方安全计算的公共数据融合创新模式研究及应用

doi:10.11959/j.issn.2096-0271.2023073

Abstract

Abstract:

Secure multi-party computation is widely used in finance, the Internet, and other fields to solve the problem of ＆quot;data silos＆quot;, but its application in the field of public data is not yet mature.An innovative model for public data integration based on secure multi-party computation was proposed, and a technical architecture for joint computing using public data from different parties while protecting their private information was presented.The model breaks through institutional constraints through technological innovation, achieving a balance between improving data value and ensuring data security.Three sub-layers of the core system of secure multi-party computation in the proposed model, including the joint computation substructure layer, the secure relational algebra layer, and the basic operator layer of secure multi-party computation were mainly analyzed.Additionally, a general process for implementing the innovative model was presented, and the practical application of the innovative model was also discussed.The results of this study provide a new reference for promoting digital China construction and facilitating the flow of data resources.

Key words: secure multi-party computation, public data, innovative integration

CLC Number:

TP399

Jiahe JIN, Chengyao ZHAO, Haoze QIU, Peng LIU. Research and application of innovative models for public data integration based on secure multi-party computation[J]. Big Data Research, 2023, 9(6): 15-27.

Figures/Tables 7

References 23

[1]	PRABHAKARAN M , SAHAI A . Secure multi-party computation[M]. Amsterdam: IOS Press, 2013.
[2]	BELLARE M , HOANG V T , ROGAWAY P . Foundations of garbled circuits[C]// Proceedings of the 2012 ACM Conference on Computer and Communications Security. New York:ACM, 2012: 784-796.
[3]	LINDELL Y , PINKAS B . A proof of security of Yao’s protocol for two-party computation[J]. Journal of Cryptology, 2009,22(2): 161-188.
[4]	MALKHI D , NISAN N , PINKAS B ,et al. Fairplay-a secure two-party computation system[C]// Proceedings of the 13th conference on USENIX Security Symposium - Volume 13. New York:ACM, 2004:20.
[5]	KOLESNIKOV V , SCHNEIDER T . Improved garbled circuit:free XOR gates and applications[C]// Proceedings of International Colloquium on Automata,Languages,and Programming. Heidelberg:Springer, 2008: 486-498.
[6]	KOLESNIKOV V , SADEGHI A R , SCHNEIDER T . Improved garbled circuit building blocks and applications to auctions and computing minima[C]// Proceedings of International Conference on Cryptology and Network Security. Heidelberg:Springer, 2009: 1-20.
[7]	BEIMEL A . Secret-sharing schemes:a survey[C]// Proceedings of International Conference on Coding and Cryptology. Heidelberg:Springer, 2011: 11-46.
[8]	LIU D , HUANG D , LUO P ,et al. New schemes for sharing points on an elliptic curve[J]. Computers ＆ Mathematics with Applications, 2008,56(6): 1556-1561.
[9]	LIN C L , HARN L . Unconditionally secure multi-secret sharing scheme[C]// Proceedings of 2012 IEEE International Conference on Computer Science and Automation Engineering. Piscataway:IEEE Press, 2012: 169-172.
[10]	RABIN M O . How to exchange secrets with oblivious transfer[J]. IACR Cryptology EPrint Archive, 2005:187.
[11]	ASHAROV G , LINDELL Y , SCHNEIDER T ,et al. More efficient oblivious transfer and extensions for faster secure computation[C]// Proceedings of the 2013 ACM SIGSAC Conference on Computer＆ Communications Security. New York:ACM, 2013: 535-548.
[12]	CHOU T , ORLANDI C . The simplest protocol for oblivious transfer[C]// Proceedings of International Conference on Cryptology and Information Security in Latin America. Cham:Springer, 2015: 40-58.
[13]	GARG S , HAJIABADI M , OSTROVSKY R . Efficient range-trapdoor functions and applications:rate-1 OT and more[C]// Proceedings of Theory of Cryptography Conference. Cham:Springer, 2020: 88-116.
[14]	CRAMER R , DAMG?RD I , NIELSEN J B . Multiparty computation from threshold homomorphic encryption[M]// Lecture notes in computer science. Heidelberg: Springer, 2001: 280-300.
[15]	GENTRY C , BONEH D . A fully homomorphic encryption scheme[M].[S.l.:s.n.], 2009.
[16]	NAEHRIG M , LAUTER K , VAIKUNTANATHAN V . Can homomorphic encryption be practical?[C]// Proceedings of the 3rd ACM workshop on Cloud computing security workshop. New York:ACM, 2011: 113-124.
[17]	CHEN C C , ZHOU J , WANG L ,et al. When homomorphic encryption marries secret sharing:secure large-scale sparse logistic regression and applications in risk control[C]// Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery ＆ Data Mining. New York:ACM, 2021: 2652-2662.
[18]	王云河, 李艺 . MPC金融应用场景[J]. 金融电子化, 2021(2): 20-22.
	WANG Y H , LI Y . Financial application scenarios of secure multi-party computation[J]. Financial Digitization, 2021(2): 20-22.
[19]	张燕, 杨一帆, 伊人 ,等. 隐私计算场景下数据质量治理探索与实践[J]. 大数据, 2022,8(5): 55-73.
	ZHANG Y , YANG Y F , YI R ,et al. Exploration and practice of data quality governance in privacy computing scenarios[J]. Big Data Research, 2022,8(5): 55-73.
[20]	张舒黎, 邓春华, 胡松 ,等. 安全多方计算体系架构及应用思考[J]. 通信技术, 2021,54(9): 2182-2189.
	ZHANG S L , DENG C H , HU S ,et al. System architecture and application thinking of secure multiparty computation[J]. Communications Technology, 2021,54(9): 2182-2189.
[21]	贾轩, 白玉真, 马智华 . 隐私计算应用场景综述[J]. 信息通信技术与政策, 2022(5): 45-52.
	JIA X , BAI Y Z , MA Z H . Overview of privacy preserving computing application scenarios[J]. Information and Communications Technology and Policy, 2022(5): 45-52.
[22]	朱智韬, 司世景, 王健宗 ,等. 联邦推荐系统综述[J]. 大数据, 2022,8(4): 105-132.
	ZHU Z T , SI S J , WANG J Z ,et al. Survey on federated recommendation systems[J]. Big Data Research, 2022,8(4): 105-132.
[23]	HAMADA K , IKARASHI D , CHIDA K ,et al. Oblivious radix sort:an efficient sorting algorithm for practical secure multi-party computation[J]. IACR Cryptology EPrint Archive,2014, 2014:121.

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Research and application of innovative models for public data integration based on secure multi-party computation

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