We study the relationship between adversarial robustness and differential privacy in highdimensional algorithmic statistics. We give the first blackbox reduction from privacy to robustness which can produce private estimators with optimal tradeoffs among sample complexity, accuracy, and privacy for a wide range of fundamental highdimensional parameter estimation problems, including mean and covariance estimation. We show that this reduction can be implemented in polynomial time in some important special cases. In particular, using nearlyoptimal polynomialtime robust estimators for the mean and covariance of highdimensional Gaussians which are based on the SumofSquares method, we design the first polynomialtime private estimators for these problems with nearlyoptimal samplesaccuracyprivacy tradeoffs. Our algorithms are also robust to a constant fraction of adversariallycorrupted samples.
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This content will become publicly available on June 20, 2024
Robustness Implies Privacy in Statistical Estimation
We study the relationship between adversarial robustness and differential privacy in highdimensional algorithmic statistics. We give the first blackbox reduction from privacy to robustness which can produce private estimators with optimal tradeoffs among sample complexity, accuracy, and privacy for a wide range of fundamental highdimensional parameter estimation problems, including mean and covariance estimation. We show that this reduction can be implemented in polynomial time in some important special cases. In particular, using nearlyoptimal polynomialtime robust estimators for the mean and covariance of highdimensional Gaussians which are based on the SumofSquares method, we design the first polynomialtime private estimators for these problems with nearlyoptimal samplesaccuracyprivacy tradeoffs. Our algorithms are also robust to a nearly optimal fraction of adversariallycorrupted samples.
more »
« less
 Award ID(s):
 2238080
 NSFPAR ID:
 10490889
 Publisher / Repository:
 55th Annual ACM Symposium on Theory of Computing (STOC)
 Date Published:
 Journal Name:
 55th Annual ACM Symposium on Theory of Computing (STOC)
 Page Range / eLocation ID:
 497506
 Format(s):
 Medium: X
 Location:
 Orlando, FL
 Sponsoring Org:
 National Science Foundation
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