The Large Margin Mechanism for Differentially Private Maximization

TL;DR

This paper introduces a range-independent, differentially private maximization algorithm that improves utility guarantees across various function classes, with applications in data mining and machine learning.

Contribution

It presents the first general-purpose, range-independent private maximization algorithm, overcoming limitations of previous range-dependent methods.

Findings

Algorithm guarantees approximate differential privacy.

Applicable to broad function classes.

Demonstrated on data mining and machine learning tasks.

Abstract

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 has been used as a sub-routine in 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. This work provides the first general-purpose, range-independent algorithm for private maximization that guarantees approximate differential privacy. Its applicability is demonstrated on two fundamental tasks in data mining and machine learning.