Large Margin Mechanism for Differentially Private Maximization
A basic problem in the design of privacy-preserving algorithms is the private maximization problem: the goal is to pick an item from a universe that (approximately) maximizes a data-dependent function, all under the constraint of differential privacy. This problem is central to many privacy-preserving algorithms for statistics and machine learning.
Previous algorithms for this problem are either range-dependent---i.e., their utility diminishes with the size of the universe---or only apply to very restricted function classes. Prof. Hsu describes a new, general-purpose and range-independent algorithm for private maximization that guarantees approximate differential privacy. He will also describe applications to private frequent itemset mining and private PAC learning.
This is joint work with Kamalika Chaudhuri and Shuang Song.