Publishing sensitive trajectory data under enhanced l-diversity model

Lin Yao, Xinyu Wang, Xin Wang, Haibo Hu, Guowei Wu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)

Abstract

With the proliferation of location-Aware devices, trajectory data have been widely collected, published, and analyzed in real-life applications. However, published trajectory data often contain sensitive attributes, so an attacker who can identify an individual from such data through record linkage, attribute linkage, or similarity attacks can gain sensitive information about this individual. To resist from these attacks, we propose a scheme called Data Privacy Preservation with Perturbation (DPPP). To protect the privacy of sensitive information, we first determine those critical location sequences that can identify specific individuals. Then we perturb these sequences by adding or deleting some moving points while ensuring the published data satisfy (l, α, β)-privacy, an enhanced privacy model from ldiversity. Our experiments on both synthetic and real-life datasets suggest that DPPP achieves better privacy while still ensuring high utility, compared with existing privacy preservation schemes on trajectory.

Original languageEnglish
Title of host publicationProceedings - 2019 20th International Conference on Mobile Data Management, MDM 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages160-169
Number of pages10
ISBN (Electronic)9781728133638
DOIs
Publication statusPublished - 10 Jun 2019
Event20th International Conference on Mobile Data Management, MDM 2019 - Hong Kong, Hong Kong
Duration: 10 Jun 201913 Jun 2019

Publication series

NameProceedings - IEEE International Conference on Mobile Data Management
Volume2019-June
ISSN (Print)1551-6245

Conference

Conference20th International Conference on Mobile Data Management, MDM 2019
Country/TerritoryHong Kong
CityHong Kong
Period10/06/1913/06/19

Keywords

  • Perturbation
  • Sensitive Label
  • Trajectory Data Publishing

ASJC Scopus subject areas

  • Engineering(all)

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