Friday, January 25, 2019

Friday, January 25, 2019 — 4:00 PM EST

The Cost of Privacy: Optimal Rates of Convergence for Parameter Estimation with Differential Privacy

With the unprecedented availability of datasets containing personal information, there are increasing concerns that statistical analysis of such datasets may compromise individual privacy. These concerns give rise to statistical methods that provide privacy guarantees at the cost of some statistical accuracy. A fundamental question is: to satisfy certain desired level of privacy, what is the best statistical accuracy one can achieve?  Standard statistical methods fail to yield sharp results, and new technical tools are called for.

In this talk, I will present a general lower bound argument to investigate the tradeoff between statistical accuracy and privacy, with application to three problems: mean estimation, linear regression and classification, in both the classical low-dimensional and modern high-dimensional settings. For these statistical problems, we also design computationally efficient algorithms that match the minimax lower bound under the privacy constraints. Finally I will show the applications of those privacy-preserving algorithms to real data containing sensitive information, such as SNPs and body fat, for which privacy-preserving statistical methods are necessary.

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