When Focus Enhances Utility: Target Range LDP Frequency Estimation and Unknown Item Discovery

TL;DR

This paper introduces a generalized framework for frequency estimation under local differential privacy, optimizing utility and privacy trade-offs, and presents novel protocols for unknown item discovery with high accuracy and efficiency.

Contribution

It proposes a unified GCMS protocol, an OCMS optimization for targeted frequencies, and a new unknown item discovery method using stability-based histograms and ESA framework.

Findings

Significantly improved privacy-accuracy-communication trade-offs.

Achieved near-central DP accuracy with local privacy guarantees.

Reduced computational costs for unknown domain data collection.

Abstract

Local Differential Privacy (LDP) protocols enable the collection of randomized client messages for data analysis, without the necessity of a trusted data curator. Such protocols have been successfully deployed in real-world scenarios by major tech companies like Google, Apple, and Microsoft. In this paper, we propose a Generalized Count Mean Sketch (GCMS) protocol that captures many existing frequency estimation protocols. Our method significantly improves the three-way trade-offs between communication, privacy, and accuracy. We also introduce a general utility analysis framework that enables optimizing parameter designs. {Based on that, we propose an Optimal Count Mean Sketch (OCMS) framework that minimizes the variance for collecting items with targeted frequencies.} Moreover, we present a novel protocol for collecting data within unknown domain, as our frequency estimation protocols only work effectively with known data domain. Leveraging the stability-based histogram technique alongside the Encryption-Shuffling-Analysis (ESA) framework, our approach employs an auxiliary server to construct histograms without accessing original data messages. This protocol achieves accuracy akin to the central DP model while offering local-like privacy guarantees and substantially lowering computational costs.