保护数据隐私的扰动方法：技术与应用综述
摘要:
扰动方法是一种数学技术，用于向数据添加受控噪声或随机性，以在允许数据分析的同时保护隐私。各种方法，例如随机响应、差异隐私、安全多方计算、噪声添加以及采样和聚合，都用于保护敏感信息免遭泄露或利用。这些方法已成功应用于机器学习、统计学和密码学，以确保数据隐私。然而，它们的实现必须经过精心设计，以避免损害数据准确性或在分析中引入偏差。大多数情况下，扰动方法为保护各个领域的数据隐私提供了一种有前途的方法。本综述概述了用于保护各个领域的数据隐私的扰动方法，包括机器学习、统计学和密码学。扰动方法涉及向数据添加受控噪声或随机性以保护隐私，同时仍允许数据分析。
Abstract: Perturbation methods are mathematical techniques used to add controlled noise or randomness to data to protect privacy while allowing data analysis. Various methods, such as randomized response, differential privacy, secure multi-party computation, noise addition, and sampling and aggregation, are used to protect sensitive information from disclosure or exploitation. These methods have been successfully applied in machine learning, statistics, and cryptography to ensure data privacy. However, their implementation must be carefully designed to avoid compromising data accuracy or introducing bias in analysis. Mostly, perturbation methods offer a promising approach to protect data privacy in various fields. This review provides an overview of perturbation methods used to protect data privacy in various fields, including machine learning, statistics, and cryptography. Perturbation methods involve adding controlled noise or randomness to data to preserve privacy while still allowing data analysis.
文章引用：吴斌. 保护数据隐私的扰动方法：技术与应用综述[J]. 计算机发展与应用, 2025, 3(2): 1-5.
致谢：
基金项目：
参考文献
[1] Newman M E J. The structure and function of complex networks, SIAM Rev, 2023, 45(2): 167-256.
[2] Kargupta Hillol, Datta Souptik, Wang Q, et al. On the privacy preserving properties of random data perturbation techniques. Proceedings - IEEE International Conference on Data Mining, ICDM, 2023: 99- 106.
[3] Warner SL. Randomized response: a survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 1965, 60(309): 63-69.
[4] Ghosh A, Roughgarden T, Sundararajan M. Universally utility-maximizing privacy mechanisms. SIAM J. Computer, 2012, 41(6): 1673-1693.
[5] Xu L, Jiang J, Wang J, et al. Information Security in Big Data: Privacy and Data Mining. IEEE Access, 2014(2): 1149–1176.
[6] Dwork C, McSherry F, Nissim K, et al. Calibrating noise to sensitivity in private data analysis. In Theory of Cryptography Conference. Springer: Berlin/Heidelberg, Germany, 2006: 265–284.
[7] Wang T, Zheng Z, Rehmani MH, et al. Privacy Preservation in Big Data from the Communication Perspective—A Survey. IEEE Communications Surveys and Tutorials, 2019, 21: 753–778.
[8] Liu C, Chen S, Zhou S, et al. A novel privacy preserving method for data publication. 2019. DOI: 10.1016/j.ins.2019.06.022.
