Trust assurance privacy preserving framework for moving objects in participatory sensing

Abstract

Nowadays, access to location-based services (LBSs) is becoming a central part of our daily life activities. For example, mobile phone users in Participatory sensing (PS) systems are requested to collect information from their nearby data collection points (DCs). A query typically reveals the identity and other personal information such as location, user profiles (e.g., race) and time. This knowledge enables an adversary PS server to deduce over time a comprehensive user location summary with a high degree of precision. Several privacy techniques in PS have been proposed recently to provide user privacy protection that assumes static objects. This conventional methods, there is yet scarcely any service, which entails the user to prove that she is at a particular location at a certain point in time. The reason for the lack of such facility lies in the fact that none of the location and time information achieved by nowadays mobile devices is trustworthy. Besides, only a few techniques that considers static objects and incorporate trust of the data collected by each user but exclude movements, thus assuming movement information has no impact on privacy. This existing work cannot guarantee complete privacy that cover knowledge attacks while serving requests by moving objects with uncertain motion pattern at the same time enhance credibility of collected data in PS systems. In this paper, we propose a trust assurance individualized framework to provide better quality protection to moving objects with nonlinear pattern in PS systems. Our experimental results demonstrate in our approach, moving user enjoy a high quality of service with a high degree of anonymity.