论文标题
当地差异隐私及其应用：一项全面调查
Local Differential Privacy and Its Applications: A Comprehensive Survey
论文作者
论文摘要
随着信息技术的快速发展，为研究和分析目的而生成了大量数据。随着越来越多的用户越来越关注他们的个人信息，隐私保护已成为要解决的紧迫问题，并引起了极大的关注。近年来，当地差异隐私（LDP）作为强大的隐私工具已被广泛部署在现实世界中。它打破了受信任的第三方的束缚，并允许用户在本地扰动其数据，从而提供更强的隐私保护。这项调查提供了当地差异隐私技术的全面概述。我们在回答各种查询和培训不同的机器学习模型的背景下总结和分析了最不发达国家中的最新研究，并比较了一系列方法。我们讨论了当地差异隐私的实际部署，并探索其在各个领域的应用。此外，我们指出了一些研究差距，并讨论了有希望的未来研究方向。
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.
