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

Near optimal routing protocols for source location privacy in wireless sensor networks: modelling, design and evaluation

Wireless Sensor Networks (WSNs) are collections of small computing devices that are used to monitor valuable assets such as endangered animals. As WSNs communicate wirelessly they leak information to malicious eavesdroppers. When monitoring assets it is important to provide Source Location Privacy (SLP), where the location of the message source must be kept hidden. Many SLP protocols have been developed by designing a protocol using intuition before evaluating its performance. However, this does not provide insight into how to develop optimal approaches. This thesis will present an alternate approach where the SLP problem is modelled using different techniques to give an optimal output. However, as this optimal output is typically for a restricted scenario, algorithms that trade optimality for generality are subsequently designed. Four main contributions are presented. First, an analysis is performed based on entropy and divergence to gain insight into how to reduce the information an attacker gains via the use of competing paths, and ways to compare the information loss of arbitrary routing protocols. Secondly, the SLP problem is modelled using Integer Linear Programming. The model result guides the design of a generic protocol called ILPRouting that groups messages together to reduce the moves an attacker makes. Thirdly, a timing analysis of when events occur is used to dynamically determine fake source parameters for the Dynamic and DynamicSPR algorithms. These fake sources lure the attacker to their location instead of the real source. Finally, the first SLP-aware duty cycle is investigated, and implemented for DynamicSPR to make it more energy efficient. These techniques are evaluated through simulations and deployments on WSN testbeds to demonstrate their effectiveness