An Efficient Source Anonymity Technique Based on Exponential Distribution Against a Global Adversary Model Using Fake Injections

The security of Wireless Sensor Networks (WSNs) is vital in several applications such as the tracking and monitoring of endangered species such as pandas in a national park or soldiers in a battlefield. This kind of applications requires the anonymity of the source, known as Source Location Privacy (SLP). The main aim is to prevent an adversary from tracing back a real event to the originator by analyzing the network traffic. Previous techniques have achieved high anonymity such as Dummy Uniform Distribution (DUD), Dummy Adaptive Distribution (DAD) and Controlled Dummy Adaptive Distribution (CAD). However, these techniques increase the overall overhead of the network. To overcome this shortcoming, a new technique is presented: Exponential Dummy Adaptive Distribution (EDAD). In this technique, an exponential distribution is used instead of the uniform distribution to reduce the overhead without sacrificing the anonymity of the source. The exponential distribution improves the lifetime of the network since it decreases the number of transmitted packets within the network. It is straightforward and easy to implement because it has only one parameter $lambda$ that controls the transmitting rate of the network nodes. The conducted adversary model is global, which has a full view of the network and is able to perform sophisticated attacks such as rate monitoring and time correlation. The simulation results show that the proposed technique provides less overhead and high anonymity with reasonable delay and delivery ratio. Three different analysis models are developed to confirm the validation of our technique. These models are visualization model, a neural network model, and a steganography model.