Local Differential Privacy and Its Applications: A Comprehensive Survey

TL;DR

This comprehensive survey reviews local differential privacy (LDP), highlighting its theoretical foundations, practical deployments, and applications across domains, while identifying research gaps and future directions.

Contribution

It provides a structured overview of LDP, compares various methods, and discusses practical deployment and future research opportunities.

Findings

LDP offers strong privacy protection by local data perturbation.

Various methods are compared for query answering and machine learning.

Practical deployment of LDP faces challenges and opportunities.

Abstract

With the fast development of Information Technology, a tremendous amount of data have been generated and collected for research and analysis purposes. As an increasing number of users are growing concerned about their personal information, privacy preservation has become an urgent problem to be solved and has attracted significant attention. Local differential privacy (LDP), as a strong privacy tool, has been widely deployed in the real world in recent years. It breaks the shackles of the trusted third party, and allows users to perturb their data locally, thus providing much stronger privacy protection. This survey provides a comprehensive and structured overview of the local differential privacy technology. We summarise and analyze state-of-the-art research in LDP and compare a range of methods in the context of answering a variety of queries and training different machine learning models. We discuss the practical deployment of local differential privacy and explore its application in various domains. Furthermore, we point out several research gaps, and discuss promising future research directions.