You are AIRing too much: Assessing the privacy of users in crowdsourcing environmental data

AuthorMineraud, Julien; Lancerin, Federico; Balasubramaniam, Sasitharan; Conti, Mauro; Tarkoma, Sasu
TypeConference Proceedings
AbstractWith the availability of inexpensive sensors, the attractiveness of participatory sensing has tremendously increased in the last decade. However, when sensing is performed with devices owned by individuals, it raises several privacy issues with respect to the data producers, and hence reduces the incentive to contribute to the service. In this paper, we evaluate the extent to which a malicious server in a crowdsourcing air quality monitoring service can track the locations of users that contribute to the service. The participants periodically send information, such as temperature, relative humidity, carbon monoxide, and luminosity of their surrounding, using an off-the-shelf sensor connected to their mobile phones. The participants also send their coarse-grain location (i.e., disclosing the ID of the cell tower to which their mobile is coupled) along with the air quality data. We evaluate the precision with which the attacker can track the participants using only air quality data. We perform a thorough analysis of the attack and show that it can accurately discover the destination of the users up to a precision of 97% (in the most ideal condition).
InThe 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-15)