Fingerprinting Mobile User Positions in Sensor Networks: Attacks and Countermeasures

We demonstrate that the network flux over the sensor network provides fingerprint information about the mobile users within the field. Such information is exoteric in the physical space and easy to access through passive sniffing. We present a theoretical model to abstract the network flux according to the statuses of mobile users. We fit the theoretical model with the network flux measurements through Nonlinear Least Squares (NLS) and develop an algorithm that iteratively approaches the NLS solution by Sequential Monte Carlo Estimation. With sparse measurements of the flux information at individual sensor nodes, we show that it is easy to identify the mobile users within the network and instantly track their movements without breaking into the details of the communicational packets. Our study indicates that most of existing systems are vulnerable to such attack against the privacy of mobile users. We further propose a set of countermeasures that redistribute and reshape the network traffic to preserve the location privacy of mobile users. With a trace driven simulation, we demonstrate the substantial threats of the attacks and the effectiveness of the proposed countermeasures.