Towards Successive Privacy Protection in Sensor Networks

With the emerging embedding of the sensor networks into the pervasive environment, our capabilities on location information gathering and processing have been greatly improved. Although this information is very useful, it also brings great challenges for protecting the privacy. Currently, most research efforts focus on protecting current location, and ignore the internal relationship among the successive locations information. To date, there are many techniques to infer a location when the related successive information is published, and which brings in serious privacy and security concerns. In this paper, we for the first time consider this kind of relationship, and identify a novel successive privacy threat. We then formulate a generic model for protecting the successive privacy. Under this model, there is a trade-off between the number of data to be published and the privacy protecting level, and which brings a novel maximum publishable location privacy problem. As this problem is intractable, we develop several heuristics. Extensive simulations demonstrate the effectiveness of our schemes.