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We consider the problem of reinforcing federated learning with formal privacy guarantees.
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B. Hitaj, G. Ateniese, and F. Pérez-Cruz, “Deep models under the gan: information leakage from collaborative deep learning,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2017, pp. 603–618
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M. J. Schneider and J. M. Abowd, “A new method for protecting interrelated time series with bayesian prior distributions and synthetic data,” Journal of the Royal Statistical Society: Series A (Statistics in Society) , vol. 178, no. 4, pp. 963–975, 2015
2015
Cited alongside, same era.
2016
Cited alongside, same era.
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2016, pp. 308–318
2016
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2016
Cited alongside, same era.
W. Wang, L. Ying, and J. Zhang, “On the relation between identifiability, differential privacy, and mutual-information privacy,” IEEE Transactions on Information Theory , vol. 62, no. 9, pp. 5018–5029, 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
M. Bun and T. Steinke, “Concentrated differential privacy: Simplifications, extensions, and lower bounds,” in Theory of Cryptography Conference . Springer, 2016, pp. 635–658
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Cited alongside, same era.
A.-S. Charest and Y. Hou, “On the meaning and limits of empirical differential privacy,” Journal of Privacy and Confidentiality , vol. 7, no. 3, p. 3, 2017
2017
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J. Geumlek, S. Song, and K. Chaudhuri, “Renyi differential privacy mechanisms for posterior sampling,” in Advances in Neural Information Processing Systems , 2017, pp. 5289–5298
2017
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M. Bun, “A teaser for differential privacy,” 2017
2017
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2018
Later among the works it cites.
M. Bun, C. Dwork, G. N. Rothblum, and T. Steinke, “Composable and versatile privacy via truncated cdp,” in Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing . ACM, 2018, pp. 74–86
2018
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2019
Closest in time.
A. Triastcyn and B. Faltings, “Generating artificial data for private deep learning,” in Proceedings of the PAL: Privacy-Enhancing Artificial Intelligence and Language Technologies, AAAI Spring Symposium Series , ser. CEUR Workshop Proceedings, no. 2335, 2019, pp. 33–40. [Online]. Available: http://ceur-ws.org/Vol-2335/1st_PAL_paper_7.pdf
2019
Closest in time.