Capturing Privacy-Preserving User Contexts with IndoorHash

Abstract

IoT devices are ubiquitous and widely adopted by end-users to gather personal and environmental data that often need to be put into context in order to gain insights. In particular, location is often a critical context information that is required by third parties in order to analyse such data at scale. However, sharing this information is i) sensitive for the user privacy and ii) hard to capture when considering indoor environments. This paper therefore addresses the challenge of producing a new location hash, named IndoorHash, that captures the indoor location of a user, without disclosing the physical coordinates, thus preserving their privacy. This location hash leverages surrounding infrastructure, such as WiFi access points, to compute a key that uniquely identifies an indoor location. Location hashes are only known from users physically visiting these locations, thus enabling a new generation of privacy-preserving crowdsourcing mobile applications that protect from third parties re-identification attacks. We validate our results with a crowdsourcing campaign of 31 mobile devices during one month of data collection.