URL: http://portal.acm.org/citation.cfm?id=1315266&jmp=cit&coll=&dl=GUIDE
http://www.winlab.rutgers.edu/~gruteser/papers/ccs308-baik.pdf ENTRY DATE: 2008-06-16 ABSTRACT: 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. We 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, we 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. We show that this approach effectively guarantees worst case tracking bounds, while achieving significant data accuracy improvements.