  Data-driven predictive control of connected and automated vehicles (CAVs) has
received increasing attention as it can achieve safe and optimal control
without relying on explicit dynamical models. However, employing the
data-driven strategy involves the collection and sharing of privacy-sensitive
vehicle information, which is vulnerable to privacy leakage and might further
lead to malicious activities. In this paper, we develop a privacy-preserving
data-enabled predictive control scheme for CAVs in a mixed traffic environment,
where human-driven vehicles and CAVs coexist. We tackle external eavesdroppers
and honest-but-curious central unit eavesdroppers who wiretap the communication
channel of the mixed traffic system and intend to infer the CAVs' state and
input information. An affine masking-based privacy protection method is
designed to conceal the true state and input signals, and an extended form of
the data-enabled predictive leading cruise control under different matrix
structures is derived to achieve privacy-preserving optimal control for CAVs.
Numerical simulations demonstrate that the proposed scheme can protect the
privacy of CAVs against attackers without affecting control performance or
incurring heavy computations.
