Differentially Private Heavy Hitter Problem
Primary Investigator:
Ninghui Li
Tianhao Wang, Ninghui Li, Somesh Jha
Abstract
Protocols satisfying Local Differential Privacy (LDP) enable parties
to collect aggregate information about a population while protecting
each user's privacy, without relying on a trusted third party. LDP
protocols (such as Google's RAPPOR) have been deployed in real-world
scenarios. In these protocols, a user encodes his private information
and perturbs the encoded value locally before sending it to an
aggregator, who combines values that users contribute to infer
statistics about the population. In this paper, we introduce a
framework that generalizes several LDP protocols proposed in the
literature. Our framework yields a simple and fast aggregation
algorithm, whose accuracy can be precisely analyzed. Our in-depth
analysis enables us to choose optimal parameters, resulting in two new
protocols (i.e., Optimized Unary Encoding and Optimized Local Hashing)
that provide better utility than protocols previously proposed. We
present precise conditions for when each proposed
protocol should be used, and perform experiments that demonstrate the advantage of our proposed protocols.