Privacy-preserving statistical estimation with optimal convergence rates

著者: Adam D. Smith - ACM Symposium on Theory of Computing, STOC 2011 被引用: 265

Consider an analyst who wants to release aggregate statistics about a data set containing sensitive information. Using differentially private algorithms guarantees that the released statistics reveal very little about any particular record in the data set. In this paper we study the asymptotic properties of differentially private algorithms for statistical inference.