Data Collection with Privacy Preserving in Participatory Sensing

Participatory sensing has increasingly become a new paradigm of data collection from a wide physical area and a large population. One of the major challenges in participatory sensing is the privacy issue. Sensing data from smartphones may contain sensitive information such as user locations. Thus, it is of great importance to preserve privacy throughout the data collection process in participatory sensing. It is however very challenging because of the distributed nature of the network, many potential malicious attackers and the convergecast model of data collection. In this paper, we present a data collection approach which preserves user privacy in participatory sensing. In this approach, a smartphone node utilizes other smartphones as intermediate nodes to transfer its sensing data. In addition, asymmetric encryption is used to prevent malicious reverse tracking along the data forwarding route, hence anonymizing the originator of the data. We analyze the security of the approach and show that it achieves a high level of security. Extensive simulations demonstrate that the proposed approach has a low overhead.