PAC Privacy: Automatic Privacy Measurement and Control of Data Processing

Author:

Xiao Hanshen,Devadas Srinivas

Publisher

Springer Nature Switzerland

Reference57 articles.

1. Abadi, M., et al.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 308–318 (2016)

2. Asi, H., Feldman, V., Koren, T., Talwar, K.: Private stochastic convex optimization: optimal rates in l1 geometry. In: International Conference on Machine Learning, pp. 393–403. PMLR (2021)

3. Asi, H., Feldman, V., Talwar, K.: Optimal algorithms for mean estimation under local differential privacy. arXiv preprint arXiv:2205.02466 (2022)

4. Balle, B., Barthe, G., Gaboardi, M.: Privacy amplification by subsampling: tight analyses via couplings and divergences. In: Advances in Neural Information Processing Systems, vol. 31 (2018)

5. Balle, B., Barthe, G., Gaboardi, M., Hsu, J., Sato, T.: Hypothesis testing interpretations and renyi differential privacy. In: International Conference on Artificial Intelligence and Statistics, pp. 2496–2506. PMLR (2020)

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