Adaptive Personalized Randomized Response Method Based on Local Differential Privacy

IF 0.5 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING

International Journal of Information Security and Privacy Pub Date : 2024-01-10 DOI:10.4018/ijisp.335225

Dongyan Zhang, Lili Zhang, Zhiyong Zhang, Zhongya Zhang

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引用次数: 0

Abstract

Aiming at the problem of adopting the same level of privacy protection for sensitive data in the process of data collection and ignoring the difference in privacy protection requirements, the authors propose an adaptive personalized randomized response method based on local differential privacy (LDP-APRR). LDP-APRR determines the sensitive level through the user scoring strategy, introduces the concept of sensitive weights for adaptive allocation of privacy budget, and realizes the personalized privacy protection of sensitive attributes and attribute values. To verify the distorted data availability, LDP-APRR is applied to frequent items mining scenarios and compared with mining associations with secrecy konstraints (MASK), and grouping-based randomization for privacy-preserving frequent pattern mining (GR-PPFM). Results show that the LDP-APRR achieves personalized protection of sensitive attributes and attribute values with user participation, and the maxPrivacy and avgPrivacy are improved by 1.2% and 4.3%, respectively, while the availability of distorted data is guaranteed.

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基于局部差异隐私的自适应个性化随机响应方法

针对数据采集过程中敏感数据采用同一级别隐私保护而忽略隐私保护要求差异的问题，作者提出了一种基于局部差分隐私的自适应个性化随机响应方法（LDP-APRR）。LDP-APRR 通过用户评分策略确定敏感等级，引入敏感权重概念自适应分配隐私预算，实现了敏感属性和属性值的个性化隐私保护。为了验证扭曲数据的可用性，LDP-APRR 被应用于频繁项挖掘场景，并与带保密约束的关联挖掘（MASK）和基于分组随机化的隐私保护频繁模式挖掘（GR-PPFM）进行了比较。结果表明，LDP-APRR 在用户参与的情况下实现了对敏感属性和属性值的个性化保护，最大隐私度和平均隐私度分别提高了 1.2% 和 4.3%，同时保证了失真数据的可用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Information Security and Privacy COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

2.50

自引率

0.00%

发文量

期刊介绍： As information technology and the Internet become more and more ubiquitous and pervasive in our daily lives, there is an essential need for a more thorough understanding of information security and privacy issues and concerns. The International Journal of Information Security and Privacy (IJISP) creates and fosters a forum where research in the theory and practice of information security and privacy is advanced. IJISP publishes high quality papers dealing with a wide range of issues, ranging from technical, legal, regulatory, organizational, managerial, cultural, ethical and human aspects of information security and privacy, through a balanced mix of theoretical and empirical research articles, case studies, book reviews, tutorials, and editorials. This journal encourages submission of manuscripts that present research frameworks, methods, methodologies, theory development and validation, case studies, simulation results and analysis, technological architectures, infrastructure issues in design, and implementation and maintenance of secure and privacy preserving initiatives.