Privacy-aware probabilistic sampling for data collection in wireless sensor networks

Abstract

The rising popularity of web services and their applications to sensor networks enables real-time data collection and queries by users. Unlike traditional periodic data collection, the traffic patterns generated from real-time data collection may expose the interests of users or the locations of unusual events to the attackers. To provide privacy in data collection, we propose a novel probabilistic sampling mechanism that can hide user queries and unusual events in the network, while supporting both routine and on-demand data reporting. Our goal is to prevent attackers from locating the unusual events and identifying interests of users by eavesdropping and analyzing the network traffic. In our probabilistic sampling scheme, the data are carefully reported at random times in order to mask the unusual events and user queries. In the meantime, our scheme can provide users with high data accuracy at minimized communication overheads. Extensive simulations have been conducted to evaluate the security strength, data accuracy and communication overheads of the proposed scheme.