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Model-based context privacy for personal data streams

Smart phones with increased computation and sensing capabilities have enabled the growth of a new generation of applications which are organic and designed to react depending on the user contexts. These contexts typically define the personal, social, work and urban spaces of an individual and are derived from the underlying sensor measurements. The shared context streams therefore embed in them information, which when stitched together can reveal behavioral patterns and possible sensitive inferences, raising serious privacy concerns. In this paper, we propose a model based technique to capture the relationship between these contexts, and better understand the privacy implications of sharing them. We further demonstrate that by using a generative model of the context streams we can simultaneously meet the utility objectives of the context-aware applications while maintaining individual privacy. We present our current implementation which uses offline model learning with online inferencing performed on the smart phone. Preliminary results are presented to provide proof-of-concept of our proposed technique.