  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.
