Netinfo Security ›› 2024, Vol. 24 ›› Issue (1): 93-105.doi: 10.3969/j.issn.1671-1122.2024.01.009
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LAI Chengzhe(), ZHAO Yining, ZHENG Dong
Received:
2023-07-16
Online:
2024-01-10
Published:
2024-01-24
Contact:
LAI Chengzhe
E-mail:lcz_xupt@163.com
CLC Number:
LAI Chengzhe, ZHAO Yining, ZHENG Dong. A Privacy Preserving and Verifiable Federated Learning Scheme Based on Homomorphic Encryption[J]. Netinfo Security, 2024, 24(1): 93-105.
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URL: http://netinfo-security.org/EN/10.3969/j.issn.1671-1122.2024.01.009
[1] | LIU Yixuan, CHEN Hong, LIU Yuhan, et al. Privacy-Preserving Techniques in Federated Learning. Journal of Software, 2022, 33(3): 1057-1092. |
刘艺璇, 陈红, 刘宇涵, 等. 联邦学习中的隐私保护技术[J]. 软件学报, 2022, 33(3):1057-1092. | |
[2] |
GODDARD M. The Eu General Data Protection Regulation (Gdpr): European Regulation That Has a Global Impact[J]. International Journal of Market Research, 2017, 59 (6): 703-705.
doi: 10.2501/IJMR-2017-050 URL |
[3] | ZHOU Chuanxin, SUN Yi, WANG Degang, et al. Survey of Federated Learning Research[J]. Chinese Journal of Network and Information Security, 2021, 7 (5): 77-92. |
周传鑫, 孙奕, 汪德刚, 等. 联邦学习研究综述[J]. 网络与信息安全学报, 2021, 7(5):77-92. | |
[4] | MCMAHAN H B, MOORE E, RAMAGE D, et al. Communication-Efficient Learning of Deep Networks from Decentralized Data[EB/OL]. (2017-02-28) [2023-06-27]. https://arxiv.org/abs/1602.05629v3. |
[5] |
KAIROUZ P, MCMAHAN H B, AVENT B, et al. Advances and Open Problems in Federated Learning[J]. Foundations and Trends® in Machine Learning, 2021, 14 (1-2): 1-210.
doi: 10.1561/2200000083 URL |
[6] | ZHOU Wei, WANG Chao, XU Jian, et al. Privacy-Preserving and Decentralized Federated Learning Model Based on the Blockchain[J]. Journal of Computer Research and Development, 2022, 59(11): 2423-2436. |
周炜, 王超, 徐剑, 等. 基于区块链的隐私保护去中心化联邦学习模型[J]. 计算机研究与发展, 2022, 59(11):2423-2436. | |
[7] | TRUONG N, SUN Kai, WANG Siyao, et al. Privacy Preservation in Federated Learning: An Insightful Survey from the Gdpr Perspective[J]. Computers & Security, 2021, 110 (4): 402-411. |
[8] | HITAJ B, ATENIESE G, PEREZ-CRUZ F. Deep Models under the Gan: Information Leakage from Collaborative Deep Learning[C]// ACM. 2017 ACM SIGSAC Conference on Computer and Communications Security. New York: ACM, 2017: 603-618. |
[9] | FU Chong, ZHANG Xuhong, JI Shouling, et al. Label Inference Attacks against Vertical Federated Learning[C]// USENIX. 31st USENIX Security Symposium, Security 2022. Berkeley: USENIX, 2022: 1397-1414. |
[10] | ZHANG Yanci, YU Han. Towards Verifiable Federated Learning[EB/OL]. (2022-02-15) [2023-06-27]. https://arxiv.org/abs/2202.08310. |
[11] | ZHANG Chengliang, LI Suyi, XIA Junzhe, et al. Batchcrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning[C]// USENIX. 2020 USENIX Annual Technical Conference. Berkeley: USENIX, 2020: 493-506. |
[12] | WANG Ning, XIAO Xiaokui, YANG Yin, et al. Collecting and Analyzing Multidimensional Data with Local Differential Privacy[C]// IEEE. 2019 IEEE 35th International Conference on Data Engineering. New York: IEEE, 2019: 638-649. |
[13] | ZHOU Chuanxin, SUN Yi, WANG Degang. Federated Learning with Gaussian Differential Privacy[C]// ACM. 2020 2nd International Conference on Robotics, Intelligent Control and Artificial Intelligence. New York: ACM, 2020: 296-301. |
[14] | BONAWITZ K, IVANOV V, KREUTER B, et al. Practical Secure Aggregation for Privacy-Preserving Machine Learning[C]// ACM. 2017 ACM SIGSAC Conference on Computer and Communications Security. New York: ACM, 2017: 1175-1191. |
[15] | MOHASSEL P, ZHANG Yupeng. Secureml: A System for Scalable Privacy-Preserving Machine Learning[C]// IEEE. 2017 IEEE Symposium on Security and Privacy. New York: IEEE, 2017: 19-38. |
[16] |
LI Ping, LI Jin, HUANG Zhengan, et al. Multi-Key Privacy-Preserving Deep Learning in Cloud Computing[J]. Future Generation Computer Systems, 2017, 74: 76-85.
doi: 10.1016/j.future.2017.02.006 URL |
[17] |
PHONG L T, AONO Y, HAYASHI T, et al. Privacy-Preserving Deep Learning via Additively Homomorphic Encryption[J]. IEEE Transactions on Information Forensics and Security, 2018, 13 (5): 1333-1345.
doi: 10.1109/TIFS.2017.2787987 URL |
[18] |
MA Xu, ZHANG Fangguo, CHEN Xiaofeng, et al. Privacy Preserving Multi-Party Computation Delegation for Deep Learning in Cloud Computing[J]. Information Sciences, 2018, 459: 103-116.
doi: 10.1016/j.ins.2018.05.005 URL |
[19] |
XU Guowen, LI Hongwei, LIU Sen, et al. Verifynet: Secure and Verifiable Federated Learning[J]. IEEE Transactions on Information Forensics and Security, 2020, 15: 911-926.
doi: 10.1109/TIFS.10206 URL |
[20] | HAN Gang, ZHANG Tiantian, ZHANG Yinghui, et al. Verifiable and Privacy Preserving Federated Learning without Fully Trusted Centers[J]. Journal of Ambient Intelligence Humanized Computing, 2021(2): 1-11. |
[21] |
GUO Xiaojie, LIU Zheli, LI Jin, et al. Verifl: Communication-Efficient and Fast Verifiable Aggregation for Federated Learning[J]. IEEE Transactions on Information Forensics and Security, 2021, 16: 1736-1751.
doi: 10.1109/TIFS.10206 URL |
[22] |
FU Anmin, ZHANG Xianglong, XIONG Naixue, et al. Vfl: A Verifiable Federated Learning with Privacy-Preserving for Big Data in Industrial IoT[J]. IEEE Transactions on Industrial Informatics, 2022, 18 (5): 3316-3326.
doi: 10.1109/TII.2020.3036166 URL |
[23] |
JIANG Changsong, XU Chunxiang, ZHANG Yuan. Pflm: Privacy-Preserving Federated Learning with Membership Proof[J]. Information Sciences, 2021, 576: 288-311.
doi: 10.1016/j.ins.2021.05.077 URL |
[24] | ZHANG Xianglong, FU Anmin, WANG Huaqun, et al. A Privacy-Preserving and Verifiable Federated Learning Scheme[C]// IEEE. ICC 2020-2020 IEEE International Conference on Communications. New York: IEEE, 2020: 1-6. |
[25] | MADI A, STAN O, MAYOUE A, et al. A Secure Federated Learning Framework Using Homomorphic Encryption and Verifiable Computing[C]// IEEE. 2021 Reconciling Data Analytics, Automation, Privacy, and Security:A Big Data Challenge. New York: IEEE, 2021: 1-8. |
[26] |
YANG Zhen, ZHOU Ming, YU Haiyang, et al. Efficient and Secure Federated Learning with Verifiable Weighted Average Aggregation[J]. IEEE Transactions on Network Science and Engineering, 2023, 10 (1): 205-222.
doi: 10.1109/TNSE.2022.3206243 URL |
[27] | YANG Qiang, LIU Yang, CHEN Tianjian, et al. Federated Machine Learning: Concept and Applications[J]. ACM Transactions on Intelligent Systems and Technology, 2019, 10: 1-19. |
[28] |
SHAMIR A. How to Share a Secret[J]. Communications of the ACM, 1979, 22 (11): 612-613.
doi: 10.1145/359168.359176 URL |
[29] |
KOBLITZ N. Elliptic Curve Cryptosystems[J]. Mathematics of Computation, 1987, 48 (177): 203-209.
doi: 10.1090/mcom/1987-48-177 URL |
[30] | MILLER V S. Use of Elliptic Curves in Cryptography[C]// Springer. Conference on the Theory and Aapplication of Cryptographic Techniques. Heidelberg: Springer, 1985: 417-426. |
[31] |
ELGAMAL T. A Public Key Cryptosystem and a Signature Scheme Based on Discrete Logarithms[J]. IEEE Transactions on Information Theory, 1985, 31 (4): 469-472.
doi: 10.1109/TIT.1985.1057074 URL |
[32] | BONEH D, GENTRY C, LYNN B, et al. Aggregate and Verifiably Encrypted Signatures from Bilinear Maps[C]// Springer. Advances in Cryptology- EUROCRYPT 2003:International Conference on the Theory and Applications of Cryptographic Techniques. Heidelberg: Springer, 2003: 416-432. |
[33] | KATE A, ZAVERUCHA G M, GOLDBERG I. Constant-Size Commitments to Polynomials and Their Applications[C]// Springer. Advances in Cryptology-ASIACRYPT 2010: 16th International Conference on the Theory and Application of Cryptology and Information Security. Heidelberg: Springer, 2010: 177-194. |
[34] | TSIOUNIS Y, YUNG M. On the Security of Elgamal Based Encryption[C]// Springer. Public Key Cryptography: First International Workshop on Practice and Theory in Public Key Cryptography, PKC’98. Heidelberg:Springer, 2006: 117-134. |
[35] | CARO A D, IOVINO V. Jpbc: Java Pairing Based Cryptography[C]// IEEE. 2011 IEEE Symposium on Computers and Communications. New York: IEEE, 2011: 850-855. |
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