移动众测中一种高实用的数据流隐私保护方案

IF 8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Zhimao Gong;Jiapeng Zhang;Haotian Wang;Mingxing Duan;Keqin Li;Kenli Li
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引用次数: 0

摘要

在移动众测中,真相发现和模式分析都是从数据流中提取有价值见解的有效方法。然而,现有的隐私保护方案要么数据效用较低,要么以较弱的隐私保护为代价提供较高的效用。为了应对这一挑战,我们引入了一个强大的隐私保护方案,该方案促进了移动众感数据流的高效用真相发现和模式分析。具体来说,我们利用方波机制,一种随机报告技术,来干扰数据以防止隐私泄露。为了减少扰动造成的效用损失,设计了一种预算分配算法。该算法确保具有近似数据的相邻时间戳共享由其累积预算派生的摄动值。此外,为了便于进行鲁棒模式分析,我们提出了一种数据分割方法,将扰动数据分成两部分:一部分随机记录模式,另一部分恢复扰动值。理论分析证实了该方案满足$\omega $ -事件$\epsilon $ -差分隐私级别。在四个真实数据集上进行的大量实验表明,我们的方案优于现有方案,在相同的隐私约束下,为真相发现和模式分析提供了更准确的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Privacy-Preserving Scheme With High Utility Over Data Streams in Mobile Crowdsensing
Both truth discovery and pattern analysis are effective methods for extracting valuable insights from data streams in mobile crowdsensing. However, existing privacy-preserving schemes either suffer from low data utility or provide high utility at the cost of weak privacy protection. To address this challenge, we introduce a robust privacy-preserving scheme that facilitates high-utility truth discovery and pattern analysis over mobile crowdsensing data streams. Concretely, we leverage the Square Wave mechanism, a randomized reporting technique, to perturb the data to prevent privacy breaches. To reduce the utility loss caused by perturbation, we design a budget allocation algorithm. This algorithm ensures that adjacent timestamps with approximate data share a perturbed value derived from their accumulated budgets. Furthermore, to facilitate robust pattern analysis, we propose a data splitting method that divides the perturbed data into two parts: one part records patterns randomly, while the other part recovers the perturbed values. Theoretical analysis confirms that our scheme satisfies $\omega $ -event $\epsilon $ -differential privacy level. Extensive experiments conducted on four real-world datasets demonstrate that our scheme outperforms existing schemes, delivering more accurate results for both truth discovery and pattern analysis under the same privacy constraints.
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来源期刊
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Information Forensics and Security 工程技术-工程:电子与电气
CiteScore
14.40
自引率
7.40%
发文量
234
审稿时长
6.5 months
期刊介绍: The IEEE Transactions on Information Forensics and Security covers the sciences, technologies, and applications relating to information forensics, information security, biometrics, surveillance and systems applications that incorporate these features
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