Quantifying Unlinkability in Multi-hop Wireless Networks
Victoria Manfredi, Cameron Donnay Hill · 2020
Consider a multi-hop wireless network in which devices act as anonymizing routers. Even if devices anonymize their link transmissions, an adversary may still be able to infer key information by observing the traffic patterns in the network. In this work, we quantify what impacts how well a Kalman-filter based adversary can infer unlinkability, that is, the probability that different pairs of devices are communicating, from anonymized link transmissions. We assume that devices do not reorder packets to mix traffic and thereby increase unlinkability. Instead, we show that traffic mixing is still possible due to the use of multi-hop routing and broadcast transmissions, with the amount of mixing dependent on the network characteristics. In simulation, we find that i) for unicast links, as network connectivity increases, unlinkability decreases, while for broadcast links as connectivity increases unlinkability increases, ii) link dynamics increase unlinkability in poorly connected topologies, iii) well-connected topologies achieve the same level of unlinkability with fewer transmissions per packet delivered, and (iv) a lattice topology has consistently good unlinkability in different scenarios.