Preserving privacy in gps traces via uncertainty-aware path cloaking

  • Authors:
  • Baik Hoh;Marco Gruteser;Hui Xiong;Ansaf Alrabady

  • Affiliations:
  • Rutgers University, Piscataway, NJ;Rutgers University, Piscataway, NJ;Rutgers University, Newark, NJ;General Motors Corporation, Warren, MI

  • Venue:
  • Proceedings of the 14th ACM conference on Computer and communications security
  • Year:
  • 2007

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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.