Personalized Privacy Aware Framework for Moving Objects in Participatory Sensing

Abstract

Mobile phone users in Participatory sensing (PS) systems are requested to collect information from their nearby locations. To ensure accurate results this exercise is well ordered and coordinated with mobile objects assigned to collect data in each data collection point. The users location information should be concealed all the times. Several techniques in PS have been proposed recently to provide user privacy protection. These techniques ignore the fact that mobile users in most cases are not stationary but rather moving objects, characterized by inevitable time varying uncertainty changes. Besides, they do not treat each user as an individual with special privilege. Users have personalized privacy requirements whose violation by other peers results in unauthorized access of information hence a privacy intrusion. Techniques for predicting the locations of moving objects exist. However majority assume continuous objects move according to linear functions. In other words, they lack generality as moving objects may follow dynamic motion patterns in real life. In practical situation mobile phone users frequently move or join/leave the PS system. Mobile objects are points that change their locations i.e., geometric coordinates over time, which demand many frequent updates of locations. It is important to capture this dynamic aspects of mobile objects in PS systems. In this paper, we incorporate a new location update policy while maintaining location comprehensive protection both peer-wise and service provider for non linear motion pattern of mobile objects in PS systems. Experimental results show that our personalized privacy framework achieves high protection while supporting moving objects queries and/or data with cloaked locations.