SLICER: A Slicing-Based K-Anonymous Privacy Preserving Scheme for Participatory Sensing

With the popularity of mobile wireless devices with various kinds of sensing abilities, a new service paradigm named Participatory Sensing has emerged to provide users with brand new life experience. However, the wide application of participatory sensing has its own challenges, among which privacy preservation and multimedia data participatory sensing are two critical problems. Unfortunately, none of the existing works has fully solved the problem of privacy preserving participatory sensing with multimedia data. In this paper, we propose SLICER, which is the first k-anonymous privacy preserving scheme for participatory sensing with multimedia data. SLICER integrates a data coding technique and message exchanging strategies, to achieve strong protection of participants' privacy, while maintaining high data accuracy. In addition, two slice transferring strategies are well designed for slice transfer to minimize the total transfer cost. Finally, we have implemented SLICER and evaluated its performance using publicly released taxi traces. Our evaluation results show that SLICER achieves high data accuracy, with low computation and communication overhead.