Perfect Contextual Information Privacy in WSNs Under Colluding Eavesdroppers
The authors address the problem of preserving contextual information privacy in Wireless Sensor Networks (WSNs). They consider an adversarial network of colluding eavesdroppers that are placed at unknown locations. Eavesdroppers use communication attributes of interest such as packet sizes, inter-packet timings, and unencrypted headers to infer contextual information, including the time and location of events reported by sensors, the sink's position, and the event type. They propose a traffic normalization technique that employs a minimum backbone set of sensors to de-correlate the observable traffic patterns from the real ones. Compared to previous works, their method significantly reduces the communication overhead for normalizing traffic patterns.