Towards privacy-sensitive participatory sensing

Abstract

The ubiquity of mobile devices has brought forth the concept of participatory sensing, whereby ordinary citizens can now contribute and share information from the urban environment. However, such applications introduce a key research challenge: preserving the location privacy of the individuals contributing data. In this paper, we propose the use of microaggregation, a concept used for protecting privacy in databases, as a solution to this problem. We compare microaggregation with tessellation, the current state-of-the-art, and demonstrate that each technique has its advantage in certain mutually exclusive situations. We propose a hybrid scheme called, Hybrid Variable-Size Maximum Distance to Average Vector (V-MDAV), which combines the positive aspects of both these techniques. Our evaluations based on real-world data traces show that hybrid V-MDAV improves the percentage of positive identifications made by the application server by up to 100% and decreases the information loss by about 40%. Furthermore, our studies show that perturbing user locations with random Gaussian noise can provide users with an extra layer of protection with very little impact on the system performance.