MaskIt: privately releasing user context streams for personalized mobile applications

Abstract

The rise of smartphones equipped with various sensors has enabled personalization of various applications based on user contexts extracted from sensor readings. At the same time it has raised serious concerns about the privacy of user contexts. In this paper, we present MASKIT, a technique to filter a user context stream that provably preserves privacy. The filtered context stream can be released to applications or be used to answer their queries. Privacy is defined with respect to a set of sensitive contexts specified by the user. MASKIT limits what adversaries can learn from the filtered stream about the user being in a sensitive context - even if the adversaries are powerful and have knowledge about the filtering system and temporal correlations in the context stream. At the heart of MASKIT is a privacy check deciding whether to release or suppress the current user context. We present two novel privacy checks and explain how to choose the one with the higher utility for a user. Our experiments on real smartphone context traces of 91 users demonstrate the high utility of MASKIT.