Spring 2019

Efficient Mean Estimation with Pure Differential Privacy via a Sum-Of-Squares Exponential Mechanism

Tuesday, March 15th, 2022, 11:00 am11:25 am

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Gautam Kamath (University of Waterloo)


Calvin Lab Auditorium

We give the first polynomial-time algorithm to estimate the mean of a d-variate probability distribution with bounded covariance from ~O(d) independent samples subject to pure differential privacy. Prior algorithms for this problem either incur exponential running time, require \Omega(d^{1.5}) samples, or satisfy only the weaker concentrated or approximate differential privacy conditions. In particular, all prior polynomial-time algorithms require d^{1+\Omega(1)} samples to guarantee small privacy loss with "cryptographically" high probability, 1-2^{-d^{\Omega(1)}}, while our algorithm retains ~O(d) sample complexity even in this stringent setting. Our main technique is a new approach to use the powerful Sum of Squares method (SoS) to design differentially private algorithms. SoS proofs to algorithms is a key theme in numerous recent works in high-dimensional algorithmic statistics -- estimators which apparently require exponential running time but whose analysis can be captured by low-degree Sum of Squares proofs can be automatically turned into polynomial-time algorithms with the same provable guarantees. We demonstrate a similar proofs to private algorithms phenomenon: instances of the workhorse exponential mechanism which apparently require exponential time but which can be analyzed with low-degree SoS proofs can be automatically turned into polynomial-time differentially private algorithms. We prove a meta-theorem capturing this phenomenon, which we expect to be of broad use in private algorithm design. Our techniques also draw new connections between differentially private and robust statistics in high dimensions. In particular, viewed through our proofs-to-private-algorithms lens, several well-studied SoS proofs from recent works in algorithmic robust statistics directly yield key components of our differentially private mean estimation algorithm.