Robustness Implies Privacy in Statistical Estimation

Author:

Hopkins Samuel B.1,Kamath Gautam2,Majid Mahbod3,Narayanan Shyam1

Affiliation:

1. Massachusetts Institute of Technology, USA

2. University of Waterloo, Canada

3. Carnegie Mellon University, USA

Funder

Natural Sciences and Engineering Research Council of Canada

University of Waterloo

Google

National Science Foundation

Division of Mathematical Sciences

Publisher

ACM

Reference56 articles.

1. Ishaq Aden-Ali , Hassan Ashtiani , and Gautam Kamath . 2021 . On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians . In Proceedings of the 32nd International Conference on Algorithmic Learning Theory (ALT ’21) . JMLR, Inc., 185–216. http://proceedings.mlr.press/v132/aden-ali21a.html Ishaq Aden-Ali, Hassan Ashtiani, and Gautam Kamath. 2021. On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians. In Proceedings of the 32nd International Conference on Algorithmic Learning Theory (ALT ’21). JMLR, Inc., 185–216. http://proceedings.mlr.press/v132/aden-ali21a.html

2. Daniel Alabi , Pravesh K Kothari , Pranay Tankala , Prayaag Venkat , and Fred Zhang . 2023 . Privately estimating a Gaussian: Efficient, robust and optimal . In Proceedings of the 55th Annual ACM Symposium on the Theory of Computing (STOC ’23) . ACM, New York, NY, USA. https://doi.org/10.48550/arXiv.2211.07503 10.48550/arXiv.2211.07503 Daniel Alabi, Pravesh K Kothari, Pranay Tankala, Prayaag Venkat, and Fred Zhang. 2023. Privately estimating a Gaussian: Efficient, robust and optimal. In Proceedings of the 55th Annual ACM Symposium on the Theory of Computing (STOC ’23). ACM, New York, NY, USA. https://doi.org/10.48550/arXiv.2211.07503

3. Hassan Ashtiani and Christopher Liaw . 2022 . Private and polynomial time algorithms for learning Gaussians and beyond . In Proceedings of the 35th Annual Conference on Learning Theory (COLT ’22) . 1075–1076. https://proceedings.mlr.press/v178/ashtiani22a.html Hassan Ashtiani and Christopher Liaw. 2022. Private and polynomial time algorithms for learning Gaussians and beyond. In Proceedings of the 35th Annual Conference on Learning Theory (COLT ’22). 1075–1076. https://proceedings.mlr.press/v178/ashtiani22a.html

4. Hilal Asi and John C Duchi . 2020. Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms. Advances in neural information processing systems, 33 ( 2020 ), 14106–14117. https://proceedings.neurips.cc/paper/2020/hash/a267f936e54d7c10a2bb70dbe6ad7a89-Abstract.html Hilal Asi and John C Duchi. 2020. Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms. Advances in neural information processing systems, 33 (2020), 14106–14117. https://proceedings.neurips.cc/paper/2020/hash/a267f936e54d7c10a2bb70dbe6ad7a89-Abstract.html

5. Hilal Asi and John C Duchi. 2020. Near instance-optimality in differential privacy. arXiv preprint arXiv:2005.10630 https://doi.org/10.48550/arXiv.2005.10630 10.48550/arXiv.2005.10630

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1. General inferential limits under differential and Pufferfish privacy;International Journal of Approximate Reasoning;2024-09

2. Private Graphon Estimation via Sum-of-Squares;Proceedings of the 56th Annual ACM Symposium on Theory of Computing;2024-06-10

3. Privately Estimating a Gaussian: Efficient, Robust, and Optimal;Proceedings of the 55th Annual ACM Symposium on Theory of Computing;2023-06-02

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