Local Differential Privacy and Its Applications: A Comprehensive Survey

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.

arXiv comment24 pages

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Cited by in corpus (16)

• Efficient Dropout-resilient Aggregation for Privacy-preserving Machine Learning
• Do Gradient Inversion Attacks Make Federated Learning Unsafe?
• A Comprehensive Survey on Local Differential Privacy Toward Data Statistics and Analysis
• Frequency Estimation under Local Differential Privacy [Experiments, Analysis and Benchmarks]
• Differentially-Private Federated Linear Bandits
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