Local differential privacy and its applications: a comprehensive survey

• Main Authors: Yang, Mengmeng, Guo, Taolin, Zhu, Tianqing, Tjuawinata, Ivan, Zhao, Jun, Lam, Kwok-Yan
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2024
• Subjects: Engineering::Computer science and engineering::Data::Data encryption; Local Differential Privacy; Private Data Statistics
• Online Access: https://hdl.handle.net/10356/172987
• Institution: Nanyang Technological University

With the rapid development of low-cost consumer electronics and pervasive adoption of next generation wireless communication technologies, a tremendous amount of data has been generated from users’ smart devices and collected for research and analysis. This inevitably results in increasing concern of mobile users regarding their personal information; the problem of privacy preservation has become more urgent and it has also attracted a significant amount of attention from both academic researchers and industry practitioners. As a strong privacy tool, local differential privacy (LDP) has been widely deployed in recent years. It eliminates the need for a trusted third party by allowing users to perturb their data locally, thus providing better privacy protection. This survey provides a comprehensive and structured overview of LDP technology. We summarize and analyse state-of-the-art development in LDP and compare a range of methods from various perspectives and from the context of machine learning model training. We explore the applications of LDP in various domains. Furthermore, we identify several research challenges and discuss promising future research directions