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 language | English (US) |
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Title of host publication | ISI 2012 - 2012 IEEE International Conference on Intelligence and Security Informatics |
Subtitle of host publication | Cyberspace, Border, and Immigration Securities |
Pages | 135-137 |
Number of pages | 3 |
DOIs | |
State | Published - Oct 17 2012 |
Externally published | Yes |
Event | 2012 10th IEEE International Conference on Intelligence and Security Informatics, ISI 2012 - Washington, DC, United States Duration: Jun 11 2012 → Jun 14 2012 |
Other
Other | 2012 10th IEEE International Conference on Intelligence and Security Informatics, ISI 2012 |
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Country/Territory | United States |
City | Washington, DC |
Period | 6/11/12 → 6/14/12 |
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Information Systems