Sensitive attribute privacy preservation of trajectory data publishing based on l-diversity

Lin Yao, Zhenyu Chen, Haibo Hu, Guowei Wu, Bin Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

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 (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.

Original languageEnglish
JournalDistributed and Parallel Databases
DOIs
Publication statusAccepted/In press - 2020

Keywords

  • Privacy preservation
  • Sensitive attribute
  • Trajectory data publishing

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Information Systems and Management

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