Abstract
Machine learning has greatly improved the convenience of modern life. As the deployment scale of machine learning grows larger, the corresponding data scale also increases, leading to a large number of small and medium-sized organizations wishing to use their respective data to train models together, even though this may bring risks of violating data privacy regulations and privacy leakage. To meet this demand, federated learning was proposed, which can satisfy the needs of various organizations to expand the training data scale without directly sharing data, while avoiding violations of data privacy regulations and privacy leakage. General federated learning usually allows clients to train local models independently, and then aggregate them on a central server to build a global model in a privacy-preserving manner. There are various ways to protect privacy, such as homomorphic encryption, differential privacy, etc. Among these methods, one type of federated learning scheme is based on homomorphic pseudorandom functions. This type of scheme is relatively simple to construct, has a smaller communication scale, is more resilient to disconnections, and has high scalability. However, the security aggregation with cryptographic primitives based on classic assumptions such as DDH cannot resist quantum attacks, and since the protected gradient vectors are usually tens of thousands of dimensions, obtaining the aggregation results requires solving tens of thousands of discrete logarithms, which leads to some loss of efficiency. In this paper, we proposed a secure aggregation scheme based on HPRG over lattice, which has practical efficiency and resilience to dropout and can resist quantum attacks due to the hardness of the RLWE assumption. Moreover, our scheme only requires polynomial multiplication and addition (usually treated as vectors in implements), thus significantly improving computational efficiency.
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References
Chandnani, N., Khairnar, C.N.: A reliable protocol for data aggregation and optimized routing in IoT WSNs based on machine learning. Wirel. Pers. Commun. 130(4), 2589–2622 (2023)
Long, Guodong, Tan, Yue, Jiang, Jing, Zhang, Chengqi: Federated Learning for Open Banking. In: Yang, Qiang, Fan, Lixin, Yu, Han (eds.) Federated Learning. LNCS (LNAI), vol. 12500, pp. 240–254. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63076-8_17
Nguyen, D.C., Ding, M., Pathirana, P.N., Seneviratne, A., Li, J., Poor, H.V.: Federated learning for internet of things: a comprehensive survey. IEEE Commun. Surv. Tutorials 23(3), 1622–1658 (2021)
Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: Concept and applications. ACM Trans. Intell. Syst. Technol. 10(2), 12:1–12:19 (2019). https://doi.org/10.1145/3298981
Zhu, L., Liu, Z., Han, S.: Deep leakage from gradients. In: Wallach, H.M., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E.B., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8–14, 2019, Vancouver, BC, Canada, pp. 14747–14756 (2019). https://proceedings.neurips.cc/paper/2019/hash/60a6c4002cc7b29142def8871531281a-Abstract.html
Oded, G.: Secure multi-party computation. manuscript. preliminary version 78(110) (1998)
Gentry, C.: Fully homomorphic encryption using ideal lattices. In: Mitzenmacher, M. (ed.) Proceedings of the 41st Annual ACM Symposium on Theory of Computing, STOC 2009, Bethesda, MD, USA, May 31 - June 2, 2009, pp. 169–178. ACM (2009). https://doi.org/10.1145/1536414.1536440
Dwork, C., Roth, A.: The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9(3–4), 211–407 (2014)
Stevens, T., Skalka, C., Vincent, C., Ring, J., Clark, S., Near, J.P.: Efficient differentially private secure aggregation for federated learning via hardness of learning with errors. In: Butler, K.R.B., Thomas, K. (eds.) 31st USENIX Security Symposium, USENIX Security 2022, Boston, MA, USA, August 10–12, 2022, pp. 1379–1395. USENIX Association (2022), https://www.usenix.org/conference/usenixsecurity22/presentation/stevens
Gehlhar, T., Marx, F., Schneider, T., Suresh, A., Wehrle, T., Yalame, H.: SAFEFL: MPC-friendly framework for private and robust federated learning. In: 2023 IEEE Security and Privacy Workshops (SPW), San Francisco, CA, USA, May 25, 2023, pp. 69–76. IEEE (2023). https://doi.org/10.1109/SPW59333.2023.00012
Jaehyoung, P., Hyuk, L.: Privacy-preserving federated learning using homomorphic encryption. Appl. Sci. 12(2) (2022). https://doi.org/10.3390/app12020734,https://www.mdpi.com/2076-3417/12/2/734
Yang, T., et al.: Applied federated learning: Improving google keyboard query suggestions. CoRR abs/1812.02903 (2018). https://arxiv.org/abs/1812.02903
Bonawitz, K.A., et al.: Practical secure aggregation for privacy-preserving machine learning. In: Thuraisingham, B., Evans, D., Malkin, T., Xu, D. (eds.) Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, CCS 2017, Dallas, TX, USA, October 30 - November 03, 2017, pp. 1175–1191. ACM (2017). https://doi.org/10.1145/3133956.3133982
Kalikinkar, M., Guang, 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)
Guo, Jiale, Liu, Ziyao, Lam, Kwok-Yan., Zhao, Jun, Chen, Yiqiang: Privacy-Enhanced Federated Learning with Weighted Aggregation. In: Lin, Limei, Liu, Yuhong, Lee, Chia-Wei. (eds.) SocialSec 2021. CCIS, vol. 1495, pp. 93–109. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-7913-1_7
Goldreich, O., Goldwasser, S., Micali, S.: How to construct random functions. In: Goldreich, O. (ed.) providing sound foundations for cryptography: On the Work of Shafi Goldwasser and Silvio Micali, pp. 241–264. ACM (2019). https://doi.org/10.1145/3335741.3335752
Liu, Z., Guo, J., Lam, K., Zhao, J.: Efficient dropout-resilient aggregation for privacy-preserving machine learning. IEEE Trans. Inf. Forensics Secur. 18, 1839–1854 (2023)
Yang, S., Chen, Y., Tu, S., Yang, Z.: A post-quantum secure aggregation for federated learning. In: Proceedings of the 12th International Conference on Communication and Network Security, ICCNS 2022, Beijing, China, December 1–3, 2022, pp. 117–124. ACM (2022). https://doi.org/10.1145/3586102.3586120
Elaine, S., T-H. Hubert, C., Eleanor, R., Richard, C., Dawn, S.: Privacy-preserving aggregation of time-series data. ACM Trans. Sen. Netw 5(3), 1–36 (2009)
Brakerski, Z., Gentry, C., Vaikuntanathan, V.: Fully homomorphic encryption without bootstrapping. Electron. Colloquium Comput. Complex. TR11-111 (2011). https://eccc.weizmann.ac.il/report/2011/111
Brakerski, Zvika: Fully Homomorphic Encryption without Modulus Switching from Classical GapSVP. In: Safavi-Naini, Reihaneh, Canetti, Ran (eds.) CRYPTO 2012. LNCS, vol. 7417, pp. 868–886. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32009-5_50
Cheon, Jung Hee, Han, Kyoohyung, Kim, Andrey, Kim, Miran, Song, Yongsoo: Bootstrapping for Approximate Homomorphic Encryption. In: Nielsen, Jesper Buus, Rijmen, Vincent (eds.) EUROCRYPT 2018. LNCS, vol. 10820, pp. 360–384. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78381-9_14
Shamir, A.: How to share a secret. Commun. ACM 22(11), 612–613 (1979)
So, J., Güler, B., Avestimehr, A.S.: Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning. IEEE J. Sel. Areas Inf. Theory 2(1), 479–489 (2021)
Bell, J.H., Bonawitz, K.A., Gascón, A., Lepoint, T., Raykova, M.: Secure single-server aggregation with (poly)logarithmic overhead. In: Ligatti, J., Ou, X., Katz, J., Vigna, G. (eds.) CCS ’20: 2020 ACM SIGSAC Conference on Computer and Communications Security, Virtual Event, USA, November 9–13, 2020, pp. 1253–1269. ACM (2020). https://doi.org/10.1145/3372297.3417885
Lyubashevsky, Vadim, Peikert, Chris, Regev, Oded: On Ideal Lattices and Learning with Errors over Rings. In: Gilbert, Henri (ed.) EUROCRYPT 2010. LNCS, vol. 6110, pp. 1–23. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13190-5_1
Banerjee, Abhishek, Fuchsbauer, Georg, Peikert, Chris, Pietrzak, Krzysztof, Stevens, Sophie: Key-Homomorphic Constrained Pseudorandom Functions. In: Dodis, Yevgeniy, Nielsen, Jesper Buus (eds.) TCC 2015. LNCS, vol. 9015, pp. 31–60. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46497-7_2
Tian, H., Wen, Y., Zhang, F., Shao, Y., Li, B.: A distributed threshold additive homomorphic encryption for federated learning with dropout resiliency based on lattice. In: Chen, X., Shen, J., Susilo, W. (eds.) Cyberspace Safety and Security - 14th International Symposium, CSS 2022, Xi’an, China, October 16–18, 2022, Proceedings. Lecture Notes in Computer Science, vol. 13547, pp. 277–292. Springer (2022). https://doi.org/10.1007/978-3-031-18067-5_20
von zur Gathen, J., Gerhard, J.: Modern Computer Algebra (3. ed.). Cambridge University Press (2013)
Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: 2017 IEEE Symp. on Secur. and Priv., SP 2017, San Jose, CA, USA, May 22–26, 2017. pp. 3–18. IEEE Comput. Soc. (2017). https://doi.org/10.1109/SP.2017.41
Froelicher, D., et al.: Scalable privacy-preserving distributed learning. Proc. Priv. Enhancing Technol. 2021(2), 323–347 (2021)
Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., Shmatikov, V.: How to backdoor federated learning. In: Chiappa, S., Calandra, R. (eds.) The 23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020, 26–28 August 2020, Online [Palermo, Sicily, Italy]. Proceedings of Machine Learning Research, vol. 108, pp. 2938–2948. PMLR (2020). https://proceedings.mlr.press/v108/bagdasaryan20a.html
Tian, H., Li, M., Ren, S.: ESE: Efficient security enhancement method for the secure aggregation protocol in federated learning. Chinese J. Electron. 32(3), 542–555 (2023). 10.23919/CJE.2021.00.370
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This work is supported by the National Natural Science Foundation of China (No. 62272491) and Guangdong Major Project of Basic and Applied Basic Research(2019B030302008).
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Zuo, R., Tian, H., Zhang, F. (2024). Post-quantum Dropout-Resilient Aggregation for Federated Learning via Lattice-Based PRF. In: Vaidya, J., Gabbouj, M., Li, J. (eds) Artificial Intelligence Security and Privacy. AIS&P 2023. Lecture Notes in Computer Science, vol 14509. Springer, Singapore. https://doi.org/10.1007/978-981-99-9785-5_27
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