Abstract
Federated learning ensures that the quality of the model is uncompromised while the resulting global model is consistent with the model trained by directly collecting user data. However, the risk of inferring data considered in federated learning. Furthermore, the inference to the learning outcome considered in a federated learning environment must satisfy that data cannot be inferred from any outcome except the owner of the data. In this paper, we propose a new federated learning scheme based on secure multi-party computation (SMC) and differential privacy. The scheme prevents inference during the learning process as well as inference of the output. Meanwhile, the scheme protects the user’s local data during the learning process to ensure the correctness of the results after users’ midway exits through the process.
Supported by the National Key Research and Development Project under Grant 2020YFB1711900, National Natural Science Foundation of China under Grant 62072065, the Key Project of Technology Innovation and Application Development of Chongqing under Grant cstc2019jscx-mbdxX0044, and the Overseas Returnees Innovation and Entrepreneurship Support Program of Chongqing under Grant cx2020004 and cx2018015.
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References
Truex, S., et al.: A hybrid approach to privacy-preserving federated learning. In: Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security, pp. 1–11 (2019)
Xiong, Z., Cai, Z., Takabi, D., Li, W.: Privacy threat and defense for federated learning with non-iid data in AIoT. IEEE Trans. Industr. Inform. (2021)
Yuwen, P., Chunqiang, H., Deng, S., Alrawais, A.: R\(^2\)peds: a recoverable and revocable privacy-preserving edge data sharing scheme. IEEE Internet Things J. 7(9), 8077–8089 (2020)
Chunqiang, H., Liao, X., Cheng, X.: Verifiable multi-secret sharing based on LFSR sequences. Theoret. Comput. Sci. 445, 52–62 (2012)
Bonawitz, K., et al.: Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1175–1191 (2017)
Hu, C., Cheng, X., Tian, Z., Yu, J., Lv, W.: Achieving privacy preservation and billing via delayed information release. IEEE/ACM Trans. Netw. (2021)
Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1310–1321 (2015)
Kairouz, P., et al.: Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977 (2019)
Pang, J., Huang, Y., Xie, Z., Han, Q., Cai, Z.: Realizing the heterogeneity: a self-organized federated learning framework for IoT. IEEE Internet Things J. 8(5), 3088–3098 (2020)
Zheng, X., Cai, Z.: Privacy-preserved data sharing towards multiple parties in industrial IoTs. IEEE J. Sel. Areas Commun. 38(5), 968–979 (2020)
Cai, Z., Xiong, Z., Xu, H., Wang, P., Li, W., Pan, Y.: Generative adversarial networks: a survey towards private and secure applications. ACM Comput. Surv. (CSUR) (2021)
Cai, Z., He, Z., Guan, X., Li, Y.: Collective data-sanitization for preventing sensitive information inference attacks in social networks. IEEE Trans. Dependable Secure Comput. 15(4), 577–590 (2016)
Aono, Y., Hayashi, T., Wang, L., Moriai, S.: Privacy-preserving deep learning via additively homomorphic encryption. IEEE Trans. Inf. Forensics Secur. 13(5), 1333–1345 (2018)
Hu, R., Guo, Y., Li, H., Pei, Q., Gong, Y.: Personalized federated learning with differential privacy. IEEE Internet Things J. 7(10), 9530–9539 (2020)
Dwork, C.: Differential privacy: a survey of results. In: Agrawal, M., Du, D., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79228-4_1
Shamir, A.: How to share a secret. Commun. ACM 22(11), 612–613 (1979)
Diffie, W., Hellman, M.: New directions in cryptography. IEEE Trans. Inf. Theory 22(6), 644–654 (1976)
Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223–238. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48910-X_16
Damgård, I., Jurik, M.: A generalisation, a simplification and some applications of Paillier’s probabilistic public-key system. In: Kim, K. (ed.) PKC 2001. LNCS, vol. 1992, pp. 119–136. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44586-2_9
Yun, A., Cheon, J.H., Kim, Y.: On homomorphic signatures for network coding. IEEE Trans. Comput. 59(9), 1295–1296 (2010)
Fiore, D., Gennaro, R., Pastro, V.: Efficiently verifiable computation on encrypted data. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pp. 844–855 (2014)
Xing, K., Chunqiang, H., Jiguo, Y., Cheng, X., Zhang, F.: Mutual privacy preserving \( k \)-means clustering in social participatory sensing. IEEE Trans. Industr. Inf. 13(4), 2066–2076 (2017)
Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 439–450 (2000)
Chunqiang, H., Li, W., Cheng, X., Jiguo, Yu., Wang, S., Bie, R.: A secure and verifiable access control scheme for big data storage in clouds. IEEE Trans. Big Data 4(3), 341–355 (2017)
Shoup, V.: Practical threshold signatures. In: Preneel, B. (ed.) EUROCRYPT 2000. LNCS, vol. 1807, pp. 207–220. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45539-6_15
Boneh, D., Franklin, M.: Identity-based encryption from the weil pairing. In: Kilian, J. (ed.) CRYPTO 2001. LNCS, vol. 2139, pp. 213–229. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44647-8_13
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Mou, W., Fu, C., Lei, Y., Hu, C. (2021). A Verifiable Federated Learning Scheme Based on Secure Multi-party Computation. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12938. Springer, Cham. https://doi.org/10.1007/978-3-030-86130-8_16
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