A Comprehensive Survey on Local Differential Privacy Toward Data Statistics and Analysis

TL;DR

This survey comprehensively reviews local differential privacy, covering theoretical foundations, mechanisms, applications, and future directions for privacy-preserving data analysis in crowdsensing.

Contribution

It provides a systematic overview of LDP models, mechanisms, and applications, highlighting recent advances and future research directions.

Findings

Various LDP mechanisms for data statistics and analysis tasks

Comparison of LDP variants for different applications

Summary of practical LDP-based scenarios

Abstract

Collecting and analyzing massive data generated from smart devices have become increasingly pervasive in crowdsensing, which are the building blocks for data-driven decision-making. However, extensive statistics and analysis of such data will seriously threaten the privacy of participating users. Local differential privacy (LDP) has been proposed as an excellent and prevalent privacy model with distributed architecture, which can provide strong privacy guarantees for each user while collecting and analyzing data. LDP ensures that each user's data is locally perturbed first in the client-side and then sent to the server-side, thereby protecting data from privacy leaks on both the client-side and server-side. This survey presents a comprehensive and systematic overview of LDP with respect to privacy models, research tasks, enabling mechanisms, and various applications. Specifically, we first provide a theoretical summarization of LDP, including the LDP model, the variants of LDP, and the basic framework of LDP algorithms. Then, we investigate and compare the diverse LDP mechanisms for various data statistics and analysis tasks from the perspectives of frequency estimation, mean estimation, and machine learning. What's more, we also summarize practical LDP-based application scenarios. Finally, we outline several future research directions under LDP.