Context-based Pseudonym Changing Scheme for Vehicular Adhoc Networks
This work addresses privacy threats for drivers in vehicular networks, but it is incremental as it builds on existing pseudonym changing methods.
The paper tackles the problem of driver privacy in vehicular adhoc networks by proposing a context-adaptive pseudonym changing scheme that dynamically adjusts based on traffic density and privacy preferences, resulting in a better compromise between traceability and quality of service compared to a random silent period scheme.
Vehicular adhoc networks allow vehicles to share their information for safety and traffic efficiency. However, sharing information may threaten the driver privacy because it includes spatiotemporal information and is broadcast publicly and periodically. In this paper, we propose a context-adaptive pseudonym changing scheme which lets a vehicle decide autonomously when to change its pseudonym and how long it should remain silent to ensure unlinkability. This scheme adapts dynamically based on the density of the surrounding traffic and the user privacy preferences. We employ a multi-target tracking algorithm to measure privacy in terms of traceability in realistic vehicle traces. We use Monte Carlo analysis to estimate the quality of service (QoS) of a forward collision warning application when vehicles apply this scheme. According to the experimental results, the proposed scheme provides a better compromise between traceability and QoS than a random silent period scheme.