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A Model for Context-Aware Location Identity Preservation Using Differential Privacy

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2 Author(s)
Assam, R. ; RWTH Aachen Univ., Aachen, Germany ; Seidl, T.

Geospatial data emanating from GPS-enabled pervasive devices reflects the mobility and interactions between people and places, and poses serious threats to privacy. Most of the existing location privacy works are based on the k-Anonymity privacy paradigm. In this paper, we employ a different and stronger privacy definition called Differential Privacy. We propose a novel context-aware and non context-aware differential privacy technique. Our technique couples Kalman filter and exponential mechanism to ensure differential privacy for spatio-temporal data. We demonstrate that our approach protects outliers and provides stronger privacy than state-of-the-art works.

Published in:

Trust, Security and Privacy in Computing and Communications (TrustCom), 2013 12th IEEE International Conference on

Date of Conference:

16-18 July 2013