A normal approximation for joint frequency estimatation under Local Differential Privacy

TL;DR

This paper introduces a new approximate estimator for joint frequency distribution estimation under Local Differential Privacy, addressing scalability challenges in high-dimensional data analysis.

Contribution

It develops a novel approximate estimator specifically designed for pure LDP protocols to improve scalability in high-dimensional joint distribution estimation.

Findings

Provides a scalable method for high-dimensional joint distribution estimation under LDP

Improves accuracy of frequency estimation in privacy-preserving data analysis

Addresses key challenges in applying LDP to complex data structures

Abstract

In the recent years, Local Differential Privacy (LDP) has been one of the corner stone of privacy preserving data analysis. However, many challenges still opposes its widespread application. One of these problems is the scalability of LDP to high dimensional data, in particular for estimating joint-distributions. In this paper, we develop an approximate estimator for frequency joint-distribution estimation under so-called pure LDP protocols.