Preserving Privacy in GPS Traces Via Uncertainty-Aware Path Cloaking

Motivated by a probe-vehicle based automotive traffic monitoring system, this paper considers the problem of guaranteed anonymity in a dataset of location traces while maintaining high data accuracy. The authors find through analysis of a set of GPS traces from 233 vehicles that known privacy algorithms cannot meet accuracy requirements or fail to provide privacy guarantees for drivers in low-density areas. To overcome these challenges, they develop a novel time-to-confusion criterion to characterize privacy in a location dataset and propose an uncertainty-aware path cloaking algorithm that hides location samples in a dataset to provide a time-to-confusion guarantee for all vehicles.