Local Differential Privacy: a tutorial

TL;DR

This tutorial provides an overview of Local Differential Privacy (LDP), a privacy-preserving approach enabling statistical analysis without trusting a central authority, highlighting algorithms for heavy hitter detection and spatial data collection.

Contribution

It offers a comprehensive overview of LDP algorithms and discusses open problems, advancing understanding of privacy-preserving data analysis techniques.

Findings

LDP enables privacy-preserving data analysis without central trust.

Algorithms for heavy hitter identification under LDP are reviewed.

Open problems in LDP are discussed for future research.

Abstract

In the past decade analysis of big data has proven to be extremely valuable in many contexts. Local Differential Privacy (LDP) is a state-of-the-art approach which allows statistical computations while protecting each individual user's privacy. Unlike Differential Privacy no trust in a central authority is necessary as noise is added to user inputs locally. In this paper we give an overview over different LDP algorithms for problems such as locally private heavy hitter identification and spatial data collection. Finally, we will give an outlook on open problems in LDP.