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The pervasive adoption of Internet-connected digital services has led to a growing concern in the personal data privacy of their customers.
Blum, M., Micali, S.: How to generate cryptographically strong sequences of pseudorandom bits. SIAM journal on Computing 13
1984
Earlier work this paper cites.
Goldreich, O., Oren, Y.: Definitions and properties of zero-knowledge proof systems. Journal of Cryptology 7
1994
Earlier work this paper cites.
Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: International conference on the theory and applications of cryptographic techniques. pp. 223–238. Springer (1999)
1999
Earlier work this paper cites.
Damgård, I., Jurik, M., Nielsen, J.B.: A generalization of paillier’s public-key system with applications to electronic voting. International Journal of Information Security 9
2010
Earlier work this paper cites.
Rastogi, V., Nath, S.: Differentially private aggregation of distributed time-series with transformation and encryption. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data. pp. 735–746 (2010)
2010
Earlier work this paper cites.
Fredrikson, M., Jha, S., Ristenpart, T.: Model inversion attacks that exploit confidence information and basic countermeasures. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security. pp. 1322–1333 (2015)
2015
Earlier work this paper cites.
Blanchard, P., El Mhamdi, E.M., Guerraoui, R., Stainer, J.: Machine learning with adversaries: Byzantine tolerant gradient descent. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. pp. 118–128 (2017)
2017
Earlier work this paper cites.
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H.B., Patel, S., Ramage, D., Segal, A., Seth, K.: 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)
2017
Earlier work this paper cites.
Cheon, J.H., Kim, A., Kim, M., Song, Y.: Homomorphic encryption for arithmetic of approximate numbers. In: International Conference on the Theory and Application of Cryptology and Information Security. pp. 409–437. Springer (2017)
2017
Cited alongside, same era.
Dua, D., Taniskidou, E.K.: Uci machine learning repository [http://archive. ics. uci. edu/ml]. university of california, irvine. School of Information and Computer Sciences (2017)
2017
Cited alongside, same era.
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. pp. 1273–1282 (2017)
2017
Cited alongside, same era.
Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: 2017 IEEE Symposium on Security and Privacy (SP). pp. 3–18. IEEE (2017)
2017
Cited alongside, same era.
2019
Later among the works it cites.
Zhu, L., Liu, Z., Han, S.: Deep leakage from gradients. In: Advances in Neural Information Processing Systems. pp. 14774–14784 (2019)
2019
Later among the works it cites.
Bell, J.H., Bonawitz, K.A., Gascón, A., Lepoint, T., Raykova, M.: Secure single-server aggregation with (poly) logarithmic overhead. In: Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security. pp. 1253–1269 (2020)
2020
Closest in time.
Chen, Y., Yang, X., Qin, X., Yu, H., Chen, B., Shen, Z.: Focus: Dealing with label quality disparity in federated learning. International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with IJCAI 2020 (2020)
2020
Closest in time.
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Gupta, O., Raskar, R.: Distributed learning of deep neural network over multiple agents. Journal of Network and Computer Applications 116
2018
Cited alongside, same era.
Mandal, K., Gong, G., Liu, C.: Nike-based fast privacy-preserving highdimensional data aggregation for mobile devices. Tech. rep., CACR Technical Report, CACR 2018-10, University of Waterloo, Canada (2018)
2018
Cited alongside, same era.
McMahan, H.B., Ramage, D., Talwar, K., Zhang, L.: Learning differentially private recurrent language models. In: International Conference on Learning Representations (2018)
2018
Cited alongside, same era.
2020
Closest in time.
Zhao, Y., Zhao, J., Yang, M., Wang, T., Wang, N., Lyu, L., Niyato, D., Lam, K.Y.: Local differential privacy based federated learning for internet of things. IEEE Internet of Things Journal (2020)
2020
Closest in time.
So, J., Güler, B., Avestimehr, A.S.: Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning. IEEE Journal on Selected Areas in Information Theory (2021)
2021
Closest in time.