A recent arXiv preprint offers a broad survey of privacy-preserving machine learning in Internet of Vehicles (IoV) applications. The authors note that ML has enhanced intelligent transportation, autonomous driving, and connected services within a large, heterogeneous network. Yet these benefits come with increased privacy risks as vehicles collect and share sensitive data.
The review is structured around fundamentals, recent advances, and future directions, aiming to consolidate knowledge in this fast-moving area. It does not present new experimental results but rather maps the current landscape of techniques and open challenges.
Because the source is a single survey paper, the article reflects its framing only. No conflicting sources were available for comparison, so the summary follows the abstract's stated scope and emphasis on the privacy-utility trade-off in IoV. Future work, the paper suggests, will need to address these tensions as connected vehicle{