The integration of Global Positioning System (GPS) receivers and sensors into mobile devices has enabled collaborative sensing applications, which monitor the dynamics of environments through opportunistic collection of data from several users’ devices. One example that motivates this paper may be a probe-vehicle-based mostly automotive traffic monitoring system, which estimates tie up from GPS velocity measurements reported from several drivers. This paper considers the matter of achieving guaranteed anonymity during a locational data set that includes location traces from several users, whereas maintaining high knowledge accuracy. We consider 2 strategies to reidentify anonymous location traces, target tracking, and home identification, and observe that known privacy algorithms cannot achieve high application accuracy requirements or fail to provide privacy guarantees for drivers in low-density areas. To overcome these challenges, we derive a novel time-to-confusion criterion to characterize privacy in a locational information set and propose a disclosure management algorithm (known as uncertainty-aware path cloaking algorithm) that selectively reveals GPS samples to limit the utmost time-toconfusion for all vehicles. Through trace-driven simulations using real GPS traces from 312 vehicles, we have a tendency to demonstrate that this algorithm effectively limits tracking risks, in specific, by eliminating tracking outliers. It additionally achieves vital knowledge accuracy improvements compared to known algorithms. We have a tendency to then present two enhancements to the algorithm. First, it also addresses the home identification risk by reducing location information revealed at the start and end of trips. Second, it also considers heading info reported by users within the tracking model. This version will therefore shield users who are moving in dense areas however during a completely different direction from the majority.
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