Title :
Preserving privacy for moving objects data mining
Author_Institution :
Div. of Software & Inf. Syst., Nanyang Technol. Univ., Singapore, Singapore
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 (ε; 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 (ε; δ)-differential privacy mechanism with the standard ε-differential privacy mechanism.
Keywords :
data mining; data privacy; geographic information systems; mobile computing; GeoLife dataset; commercial purposes; differentially private mechanism; frequent location pattern mining algorithm; geopositioning capability; mobile devices; moving object trajectories; moving objects data mining; privacy preservation; security purposes; Data privacy; Databases; Noise; Privacy; Sensitivity; Upper bound;
Conference_Titel :
Intelligence and Security Informatics (ISI), 2012 IEEE International Conference on
Conference_Location :
Arlington, VA
Print_ISBN :
978-1-4673-2105-1
DOI :
10.1109/ISI.2012.6284198