Abstract
With the fast growth of quantum computers in recent years, the use of post-quantum cryptography has steadily become vital. In the privacy-preserving aggregation of federated learning, different cryptographic primitives, such as encryption, key agreement, digital signature, secret-sharing, and pseudorandom generator, may be used to protect users’ privacy. However, in the original privacy-preserving aggregation protocol, the classical cryptographic primitives are vulnerable to quantum attacks. Bonawitz et al. presented the SecAgg (Secure Aggregation) protocol, which is based on double-masking MPC (Multi-Party Computation). On this basis, we took advantage of the structure of the original protocol and the properties of lattice cryptography to apply lattice cryptography schemes to the protocol and presented a lattice-based post-quantum privacy-preserving aggregation protocol, which can withstand quantum attacks.
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References
Yang, Q., et al.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. (TIST) 10(2), 1–19 (2019)
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 (2017)
Shor, P.W.: Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Rev. 41(2), 303–332 (1999)
Bernstein, D.J.: Introduction to post-quantum cryptography. In: Bernstein, D.J., Buchmann, J., Dahmen, E. (eds.) Post-Quantum Cryptography, pp. 1–14. Springer, Berlin, Heidelberg (2009) . https://doi.org/10.1007/978-3-540-88702-7_1
De Feo, L.: Mathematics of isogeny based cryptography. arXiv preprint arXiv:1711.04062 (2017)
Shamir, A.: How to share a secret. Commun. ACM 22(11), 612–613 (1979)
Bos, J., et al.: Frodo: Take off the ring! Practical, quantum-secure key exchange from LWE. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (2016)
CYBER: Quantum-Safe Key Exchanges (V1.1.1). ETSI TR 103 570–2017 (2017)
Diffie, W., Hellman, M.E.: New directions in cryptography. IEEE Trans. Inf. Theory 22(6), 644–654 (1976)
Fouque, P.-A., et al.: Falcon: fast-Fourier lattice-based compact signatures over NTRU. Submiss. NIST’s Post-quantum Cryptogr. Standard. Process. 36(5), 1–7 (2018)
Grover, L.K.: From Schrödinger’s equation to the quantum search algorithm. Pramana 56(2), 333–348 (2001)
Dworkin, M.J., et al.: Advanced encryption standard (AES) (2001)
Applebaum, B., Cash, D., Peikert, C., Sahai, A.: Fast cryptographic primitives and circular-secure encryption based on hard learning problems. In: Halevi, S. (ed.) CRYPTO 2009. LNCS, vol. 5677, pp. 595–618. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03356-8_35
Regev, O.: On lattices, learning with errors, random linear codes, and cryptography. J. ACM (JACM) 56(6), 1–40 (2009)
Dworkin, M.J.: Recommendation for block cipher modes of operation: Galois/Counter Mode (GCM) and GMAC (2007)
Choi, B., et al.: Communication-computation efficient secure aggregation for federated learning. arXiv preprint arXiv:2012.05433 (2020)
Alexandru, A.B., Pappas, G.J.: Private weighted sum aggregation for distributed control systems. IFAC-PapersOnLine 53(2), 11081–11088 (2020)
Stripelis, D., et al.: Secure neuroimaging analysis using federated learning with homomorphic encryption. In: 17th International Symposium on Medical Information Processing and Analysis, vol. 12088. SPIE (2021)
Acknowledgements
This work is supported by Guangdong Major Project of Basic and Applied Basic Research (2019B030302008) and the National Natural Science Foundation of China (No. 61972429).
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Zuo, R., Tian, H., An, Z., Zhang, F. (2022). Post-quantum Privacy-Preserving Aggregation in Federated Learning Based on Lattice. In: Chen, X., Shen, J., Susilo, W. (eds) Cyberspace Safety and Security. CSS 2022. Lecture Notes in Computer Science, vol 13547. Springer, Cham. https://doi.org/10.1007/978-3-031-18067-5_23
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