Quantifying Source Location Privacy Routing Performance via Divergence and Information Loss

Abstract

Source location Privacy (SLP) is an important property for security critical applications deployed over a wireless sensor network. This property specifies that the location of the source of messages needs to be kept secret from an eavesdropping adversary that is able to move around the network. Most previous work on SLP has focused on developing protocols to enhance the SLP imparted to the network under various attacker models and other conditions. Other works have focused on analysing the level of SLP being imparted by a specific protocol. In this paper, we introduce the notion of a routing matrix which captures when messages are first received. We then introduce a novel approach where an optimal SLP routing matrix is derived. In this approach, the attacker's movement is modelled as a Markov chain where measures of conditional entropy and divergence are used to compare routing matrices and quantify if they provide high levels of SLP. We propose the notion of a properly competing paths that causes an attacker to divert when moving towards the source. This concept provides the basis for developing a perturbation model, similar to those used in privacy-preserving data mining. We formally prove that properly competing paths are both necessary and sufficient in ensuring the existence of an SLP-aware routing matrix and show their usage in developing an SLP-aware routing matrix. Further, we show how different SLP-aware routing matrices 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.

A source location privacy protocol‐based energy‐efficient and link‐reliable multi‐scale bifurcated deep Capsnet routing in social Internet of Things

“…The evaluation metrics of this approach shown that the SP, lifetime of the network and delay of this method are improved than the compared approaches but need to analyze the performance with various network topologies like, elliptical and circular. In 2022, Bradbury and Jhumka 5 suggested an SLP quantifying performance of routing via loss of information and divergence. This approach proved that competing the paths properly was important.…”

“…These attacks make the source location privacy (SLP) important for SIoT. 4,5 WSN has a great submission value for health care, military monitoring, and national security. 6 However, the privacy issues of the WSN become the key bottlenecks for its further improvement.…”

“…The SIoT faces several threats that affect its security, and some of the threats are node compromising, route disrupting, eavesdropping, and data fabricating. These attacks make the source location privacy (SLP) important for SIoT 4,5 …”