  It has been demonstrated that leading cruise control (LCC) can improve the
operation of mixed-autonomy platoons by allowing connected and automated
vehicles (CAVs) to make longitudinal control decisions based on the information
provided by surrounding vehicles. However, LCC generally requires surrounding
human-driven vehicles (HDVs) to share their real-time states, which can be used
by adversaries to infer drivers' car-following behavior, potentially leading to
financial losses or safety concerns. This paper aims to address such privacy
concerns and protect the behavioral characteristics of HDVs by devising a
parameter privacy-preserving approach for mixed-autonomy platoon control.
First, we integrate a parameter privacy filter into LCC to protect sensitive
car-following parameters. The privacy filter allows each vehicle to generate
seemingly realistic pseudo states by distorting the true parameters to pseudo
parameters, which can protect drivers' privacy in behavioral parameters without
significantly influencing the control performance. Second, to enhance the
practicality and reliability of the privacy filter within LCC, we first extend
the current approach to accommodate continuous parameter spaces through a
neural network estimator. Subsequently, we introduce an individual-level
parameter privacy preservation constraint, focusing on the privacy level of
each individual parameter pair, further enhancing the approach's reliability.
Third, analysis of head-to-tail string stability reveals the potential impact
of privacy filters in degrading mixed traffic flow performance. Simulation
shows that this approach can effectively trade off privacy and control
performance in LCC. We further demonstrate the benefit of such an approach in
networked systems, i.e., by applying the privacy filter to a proceeding
vehicle, one can also achieve a certain level of privacy for the following
vehicle.
