Preserving privacy for moving objects data mining

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

The prevalence of mobile devices with geopositioning capability has resulted in the rapid growth in the amount of moving object trajectories. These data have been collected and analyzed for both commercial (e.g., recommendation system) and security (e.g. surveillance and monitoring system) purposes. One needs to ensure the privacy of these raw trajectory data and the derived knowledge by not disclosing or releasing them to adversary. In this paper, we propose a practical implementation of a (∈, δ)-differentially private mechanism for moving objects data mining; in particular, we apply it to the frequent location pattern mining algorithm. Experimental results on the real-world GeoLife dataset are used to compare the performance of the (∈, δ)-differential privacy mechanism with the standard ∈-differential privacy mechanism.

Original languageEnglish (US)
Title of host publicationISI 2012 - 2012 IEEE International Conference on Intelligence and Security Informatics
Subtitle of host publicationCyberspace, Border, and Immigration Securities
Pages135-137
Number of pages3
DOIs
StatePublished - Oct 17 2012
Externally publishedYes
Event2012 10th IEEE International Conference on Intelligence and Security Informatics, ISI 2012 - Washington, DC, United States
Duration: Jun 11 2012Jun 14 2012

Other

Other2012 10th IEEE International Conference on Intelligence and Security Informatics, ISI 2012
Country/TerritoryUnited States
CityWashington, DC
Period6/11/126/14/12

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems

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