The widely application of positioning technology has made collecting the movement of people feasible for knowledge-based decision. Data in its original form often contain sensitive attributes and publishing such data will leak individuals’ privacy. Especially, a privacy threat occurs when an attacker can link a record to a specific individual based on some known partial information. Therefore, maintaining privacy in the published data is a critical problem. To prevent record linkage, attribute linkage, and similarity attacks based on the background knowledge of trajectory data, we propose a data privacy preservation with enhanced l-diversity. First, we determine those critical spatial-temporal sequences which are more likely to cause privacy leakage. Then, we perturb these sequences by adding or deleting some spatial-temporal points while ensuring the published data satisfy our (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L,\alpha ,\beta $$\end{document}L,α,β)-privacy, an enhanced privacy model from l-diversity. Our experiments on both synthetic and real-life datasets suggest that our proposed scheme can achieve better privacy while still ensuring high utility, compared with existing privacy preservation schemes on trajectory.

中文翻译：

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基于l-diversity的轨迹数据发布敏感属性隐私保护

定位技术的广泛应用，使得采集人的运动信息可以进行基于知识的决策。原始形式的数据通常包含敏感属性，发布此类数据会泄露个人隐私。特别是，当攻击者可以根据一些已知的部分信息将记录链接到特定个人时，就会发生隐私威胁。因此，维护已发布数据的隐私是一个关键问题。为了防止基于轨迹数据背景知识的记录链接、属性链接和相似性攻击，我们提出了一种增强 l-diversity 的数据隐私保护。首先，我们确定那些更可能导致隐私泄露的关键时空序列。然后，我们通过添加或删除一些时空点来扰乱这些序列，同时确保发布的数据满足我们的 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \ usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L,\alpha ,\beta $$\end{document}L,α,β )-privacy，来自 l-diversity 的增强隐私模型。我们对合成数据集和真实数据集的实验表明，与现有的轨迹隐私保护方案相比，我们提出的方案可以实现更好的隐私，同时仍然确保高实用性。