Abstract
Federated learning is one of the cutting-edge research area in machine learning in era of big data. In federated learning, multiple clients rely on an untrusted server for model training in a distributed environment. Instead of sending local data directly to the server, the client achieves the effect of traditional centralized learning by sharing optimized parameters that represent the local model. However, the data used to train the model by the client holds private information of individuals. A potential adversary can steal the clients’ model parameters by corrupting the server, then recover clients’ local training data or reconstruct their local models. In order to solve the aforementioned problems, we construct an efficient federated learning framework based on multi-key homomorphic encryption, which can effectively restrict the adversary from accessing the clients’ model. In this framework, using homomorphic encryption ensures that all operations, including the server-side aggregation process, are secure and do not reveal any private information about the training data. At the same time, we consider multi-key scenario, where each client does not need to share the same public key and private key, but each of them has its own public-private key pair. It is convenient for the client to join the model update or be offline at any time, which greatly increases the flexibility and scalability of the system. Security and efficiency analysis indicates that the proposed framework is secure and efficient.
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References
Zhu, H., Jin, Y.: Multi-objective evolutionary federated learning. IEEE Trans. Neural Netw. Learn. Syst. 31(4), 1310–1322 (2019)
Voigt, P., von dem Bussche, A.: The EU General Data Protection Regulation (GDPR). Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57959-7
Hard, A., et al.: Federated learning for mobile keyboard prediction, arXiv preprintarXiv:1811.03604 (2018)
Corrado, G.: Computer, respond to this email. Google Res. Blog 03–11 (2015)
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)
Li, T., Sahu, A.K., Talwalkar, A., Smith, V.: Federated learning: challenges, methods, and future directions. IEEE Sig. Process. Mag. 37(3), 50–60 (2020)
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)
Hitaj, B., Ateniese, G., Perez-Cruz, F.: Deep models under the gan: information leakage from collaborative deep learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 603–618 (2017)
Nasr, M., Shokri, R., Houmansadr, A.: Comprehensive privacy analysis of deep learning: Stand-alone and federated learning under passive and active white-box inference attacks. arXiv preprint arXiv:1812.00910 (2018)
Geyer, R.C., Klein, T., Nabi, M.: Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557 (2017)
McMahan, H.B., Ramage, D., Talwar, K., Zhang, L.: Learning differentially private recurrent language models. arXiv preprint arXiv:1710.06963 (2017)
Roth, E., Noble, D., Falk, B.H., Haeberlen, A.: Honeycrisp: large-scale differentially private aggregation without a trusted core. In: Proceedings of the 27th ACM Symposium on Operating Systems Principles, pp. 196–210 (2019)
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
Bhowmick, A., Duchi, J., Freudiger, J., Kapoor, G., Rogers, R.: Protection against reconstruction and its applications in private federated learning. arXiv preprint arXiv:1812.00984 (2018)
Phong, L.T., Aono, Y., Hayashi, T., Wang, L., Moriai, S.: Privacy-preserving deep learning via additively homomorphic encryption. IEEE Trans. Inf. Forensics Secur. 99, 1 (2017)
Shamir, A.: How to share a secret. Commun. ACM 22(11), 612–613 (1979)
Peter, A., Tews, E., Katzenbeisser, S.: Efficiently outsourcing multiparty computation under multiple keys. IEEE Trans. Inf. Forensics Secur. 8(12), 2046–2058 (2013)
McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, PMLR 2017, pp. 1273–1282
Konečnỳ, J., McMahan, H.B., Yu, F.X., Richtárik, P., Suresh, A.T., Bacon, D.: Federated learning: Strategies for improving communication efficiency, arXiv preprint arXiv:1610.05492 (2016)
Learning, F.: Collaborative machine learning without centralized training data (2016)
Rivest, R.L., Adleman, L., Dertouzos, M.L., et al.: On data banks and privacy homomorphisms. Found. Secure Comput. 4(11), 169–180 (1978)
Gentry, C.: Fully homomorphic encryption using ideal lattices. In: Proceedings of the forty-first annual ACM symposium on Theory of computing, pp. 169–178 (2009)
Bresson, E., Catalano, D., Pointcheval, D.: A simple public-key cryptosystem with a double trapdoor decryption mechanism and its applications. In: Laih, C.-S. (ed.) ASIACRYPT 2003. LNCS, vol. 2894, pp. 37–54. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-40061-5_3
Mandal, K., Gong, G.: Privfl: practical privacy-preserving federated regressions on high-dimensional data over mobile networks. In: Proceedings of the 2019 ACM SIGSAC Conference on Cloud Computing Security Workshop, pp. 57–68 (2019)
Acknowledgement
This work is supported by National Natural Science Foundation of China (No. 61702218, 61672262), China Scholarship Council (No. 201808370046), Shandong Provincial Key Research and Development Project (No. 2019GGX101028, 2018CXGC0706), Natural Science Foundation of Shandong Province (No. ZR2019LZH015), Shandong Province Higher Educational Science and Technology Program (No. J18KA349), Project of Independent Cultivated Innovation Team of Jinan City (No. 2018GXRC002).
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Zhang, Q., Jing, S., Zhao, C., Zhang, B., Chen, Z. (2022). Efficient Federated Learning Framework Based on Multi-Key Homomorphic Encryption. In: Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2021. Lecture Notes in Networks and Systems, vol 343. Springer, Cham. https://doi.org/10.1007/978-3-030-89899-1_10
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