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Creators/Authors contains: "Majid, Mahbod"

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  1. ; ; ; ; ; (, Society for Industrial and Applied Mathematics)
    Free, publicly-accessible full text available January 1, 2026
  2. ; ; ; (, 55th Annual ACM Symposium on Theory of Computing (STOC))
    We study the relationship between adversarial robustness and differential privacy in high-dimensional algorithmic statistics. We give the first black-box 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 high-dimensional 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 nearly-optimal polynomial-time robust estimators for the mean and covariance of high-dimensional Gaussians which are based on the Sum-of-Squares method, we design the first polynomial-time private estimators for these problems with nearly-optimal samples-accuracy-privacy tradeoffs. Our algorithms are also robust to a constant fraction of adversarially-corrupted samples. 
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    Accepted Manuscript Full Text Available