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The rapid expansion of location-based services gives rise to significant security and privacy apprehensions. While these services deliver convenience, they accentuate concerns regarding widespread location tracking via web services, mobile apps, IoT devices, and autonomous vehicles. In this study, we comprehensively assess the merits and constraints of prevalent techniques in location privacy protection, including spatial-temporal cloaking, k-anonymity, differential privacy, and encryption. Furthermore, we delve into emerging applications like intelligent traffic planning and virus contact tracing which introduce novel complexities to the pursuit of robust location privacy safeguards. 

With the advances in autonomous vehicles and intelligent intersection management systems, traffic lights may be replaced by optimal travel plans calculated for each passing vehicle in the future. While these technological advancements are envisioned to greatly improve travel efficiency, they are still facing various challenging security hurdles since even a single deviation of a vehicle from its assigned travel plan could cause a serious accident if the surrounding vehicles do not take necessary actions in a timely manner. In this paper, we propose a novel security mechanism namely NWADE which can be integrated into existing autonomous intersection management systems to help detect malicious vehicle behavior and generate evacuation plans. In the NWADE mechanism, we introduce the neighborhood watch concept whereby each vehicle around the intersection will serve as a watcher to report or verify the abnormal behavior of any nearby vehicle and the intersection manager. We propose a blockchainbased verification framework to guarantee the integrity and trustworthiness of the individual travel plans optimized for the entire intersection. We have conducted extensive experimental studies on various traffic scenarios, and the experimental results demonstrate the practicality, effectiveness, and efficiency of our mechanism.