  Vehicular ad hoc network (VANET) is an enabling technology in modern
transportation systems for providing safety and valuable information, and yet
vulnerable to a number of attacks from passive eavesdropping to active
interfering. Intrusion detection systems (IDSs) are important devices that can
mitigate the threats by detecting malicious behaviors. Furthermore, the
collaborations among vehicles in VANETs can improve the detection accuracy by
communicating their experiences between nodes. To this end, distributed machine
learning is a suitable framework for the design of scalable and implementable
collaborative detection algorithms over VANETs. One fundamental barrier to
collaborative learning is the privacy concern as nodes exchange data among
them. A malicious node can obtain sensitive information of other nodes by
inferring from the observed data. In this paper, we propose a
privacy-preserving machine-learning based collaborative IDS (PML-CIDS) for
VANETs. The proposed algorithm employs the alternating direction method of
multipliers (ADMM) to a class of empirical risk minimization (ERM) problems and
trains a classifier to detect the intrusions in the VANETs. We use the
differential privacy to capture the privacy notation of the PML-CIDS and
propose a method of dual variable perturbation to provide dynamic differential
privacy. We analyze theoretical performance and characterize the fundamental
tradeoff between the security and privacy of the PML-CIDS. We also conduct
numerical experiments using the NSL-KDD dataset to corroborate the results on
the detection accuracy, security-privacy tradeoffs, and design.
