Query Privacy in Sensing-as-a-Service Platforms

Abstract : The Internet of Things (IoT) promises to revolutionize the way we interact with the physical world. Even though this paradigm is still far from being completely realized, there already exist Sensing-as-a-Service (S$$^2$$2aaS) platforms that allow users to query for IoT data. While this model offers tremendous benefits, it also entails increasingly challenging privacy issues. In this paper, we concentrate on the protection of user privacy when querying sensing devices through a semi-trusted S$$^2$$2aaS platform. In particular, we build on techniques inspired by proxy re-encryption and k-anonymity to tackle two intertwined problems, namely query privacy and query confidentiality. The feasibility of our solution is validated both analytically and empirically.