Local Differential Privacy and Its Applications: A Comprehensive Survey

2020/08/09 by Mengmeng Yang, Yang, Mengmeng, Lingjuan Lyu +7 · 9 citations
Computer Science · Social Sciences · #Cryptography and Data Security #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Privacy, Security, and Data Protection #Privacy-Preserving Technologies in Data

paper · pdf · doi:10.48550/arxiv.2008.03686

openalex publication_date 2020/08/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Kronecker Graphs: An Approach to Modeling Networks
• Differentially Private Federated Learning: A Client Level Perspective
• Local Differential Privacy: a tutorial
• Differential Privacy-enabled Federated Learning for Sensitive Health Data
• Locally Private Distributed Reinforcement Learning

Cited by

• Mutual Information Bounds in the Shuffle Model
• Online Linear Regression with Paid Stochastic Features
• Bayesian Advantage of Re-Identification Attack in the Shuffle Model
• PEEL: A Poisoning-Exposing Encoding Theoretical Framework for Local Differential Privacy
• High-Probability Bounds For Heterogeneous Local Differential Privacy
• An information theorist's tour of differential privacy
• FedRP: A Communication-Efficient Approach for Differentially Private Federated Learning Using Random Projection
• Locally Differentially Private Thresholding Bandits
• Differentially-Private Federated Linear Bandits

Related

• A Survey of Local Differential Privacy and Its Variants
• Local Privacy-preserving Mechanisms and Applications in Machine Learning
• Local Differential Privacy: a tutorial
• A Comprehensive Guide to Differential Privacy: From Theory to User Expectations
• Post-Processing in Local Differential Privacy: An Extensive Evaluation and Benchmark Platform