Recent research in VANET privacy addresses the critical need to protect drivers’ identities, locations, and behavior patterns amid frequent vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications in dynamic environments. Studies explore advanced techniques such as pseudonym-changing schemes, group-based anonymous communication, and federated learning frameworks that allow vehicles to participate in data sharing or model training without exposing raw data. Privacy-preserving protocols increasingly integrate blockchain for decentralized accountability and zero-knowledge proofs to authenticate vehicles anonymously. Researchers are also investigating schemes tailored for low-density or urban highway scenarios where traditional anonymity sets shrink, leveraging techniques like virtual trajectory generation and dynamic grouping to bolster location privacy. These advances aim to balance rigorous privacy safeguards with the latency, resource, and mobility constraints inherent in vehicular networks.