TrustMeter: A trust assessment scheme for collaborative privacy mechanisms in participatory sensing applications

In typical participatory sensing applications, mobile devices record a variety of sensor readings (e.g., sound samples and accelerometer data), which are tagged with spatiotemporal information and uploaded to an application server. The collection of detailed location data reveal insights about the users' whereabouts and daily routines, therefore seriously compromising their privacy. Users can mutually preserve their privacy by opportunistically exchanging sensor readings during physical meetings, thus breaking the link between the collected data and their permanent identities. The success of this procedure depends on the collaboration of all participating users. Our paper proposes a scheme called TrustMeter to assess the individual user contribution to this privacy protection mechanism. Based on peer-based ratings, our system attributes trust levels to each user allowing to readily identify and quarantine malicious users. We investigate the TrustMeters performance under different attacks by means of extensive simulations, and show that it succeeds in quarantining malicious users in most analyzed scenarios.