Understanding source location privacy protocols in sensor networks via perturbation of Time Series

Source location privacy (SLP) is becoming an important property for a large class of security-critical wireless sensor network applications such as monitoring and tracking. Much of the previous work on SLP has focused on the development of various protocols to enhance the level of SLP imparted to the network, under various attacker models and other conditions. Other work has focused on analysing the level of SLP being imparted by a specific protocol. In this paper, we adopt a different approach where we model the attacker movement as a time series and use information theoretic concepts to infer the properties of a routing protocol that imparts high levels of SLP. We propose the notion of a properly competing path that causes an attacker to “stall” when moving towards the source. This concept provides the basis for developing a perturbation model, similar to those in privacy-preserving data mining. We then show how to use properly competing paths to develop properties of an SLP-aware routing protocol. Further, we show how different SLP-aware routing protocols can be obtained through different instantiations of the framework. Those instantiations are obtained based on a notion of information loss achieved through the use of the perturbation model proposed