Locally Differentially Private Heavy Hitter Identification

TL;DR

This paper introduces a new locally differentially private protocol called Prefix Extending Method (PEM) for identifying heavy hitters efficiently in large domains, balancing privacy and utility.

Contribution

The paper proposes the PEM protocol that enables heavy hitter identification under LDP by using prefix reporting, with analysis of optimal parameters and design principles.

Findings

PEM outperforms existing methods on synthetic datasets.

PEM achieves high utility on real-world datasets.

The protocol maintains strong privacy guarantees.

Abstract

The notion of Local Differential Privacy (LDP) enables users to answer sensitive questions while preserving their privacy. The basic LDP frequent oracle protocol enables the aggregator to estimate the frequency of any value. But when the domain of input values is large, finding the most frequent values, also known as the heavy hitters, by estimating the frequencies of all possible values, is computationally infeasible. In this paper, we propose an LDP protocol for identifying heavy hitters. In our proposed protocol, which we call Prefix Extending Method (PEM), users are divided into groups, with each group reporting a prefix of her value. We analyze how to choose optimal parameters for the protocol and identify two design principles for designing LDP protocols with high utility. Experiments on both synthetic and real-world datasets demonstrate the advantage of our proposed protocol.