Privacy-respecting discovery of data providers in crowd-sensing applications

Crowd-sensing applications are based on the contribution of user-related context information and as such, they are particularly vulnerable to privacy-compromising attacks. In this paper we focus on the problem of information discovery by data consumers who can pose queries to mobile users providing sensed data. The way to protect the privacy of these mobile users is through the use of cloud-based agents, which obfuscate user location and enforce the sharing practices of their owners. The cloud agents organise themselves in a structure, namely a quadtree, that enables queriers to contact directly the mobile users in the area of interest and, based on their own criteria, select the ones to get sensing data from. The tree is kept in a decentralized manner, stored and maintained by the mobile agents themselves, thus avoiding the privacy implications of previous, centralized techniques. Our proposed solution complements and expands upon prior work in the area while it is shown experimentally to be both scalable, efficient and easy to maintain.