1. Preserving Privacy GPS Traces via Uncertainty-Aware Path Cloaking Baik Hoh, Marco Gruteser, Hui Xiong, Ansaf Alrabady Presenter:Yao Lu ECE 256, Spring 11 Duke University

2. 2 Overview Introduction Problem Statement Previous work Proposed method Evaluation Discussion

3. Motivation

6. Adversary Model Use successive location samples from a vehicle to reconstruct its path mix of various samples belonging to several vehicles. Predict the target position using the last known speed and heading information and then decide which next sample to link to the same vehicle. If multiple candidate samples exist, choose the one with the highest a posteriori probability based on a probability model of distance and time deviations from the prediction. If several of these samples appear similar to each other, no decision with high certainty is possible and tracking stops.

7. Problem Statement Objective 1.Privacy Protection: Guarantee strong anonymity in high and low density areas 2.Data quality: Provide sufficient information for traffic monitoring Assumptions 1.Trustworthy server to execute centralized algorithm 2.Adversary has no priori information of the tracking subject

8. When two paths cross

9. Existing privacy algorithms K-anonymity: to generalize a data record until it is indistinguishable from the records of at least k-1 other individuals

10. Existing privacy algorithms Subsampling

11. Privacy Metrics Mean Time To Confusion (MTTC) Tracking Uncertainty

12. Uncertainty calculation 1

14. Uncertainty calculation 2

16. Path Privacy-Preserving Mechanism Only reveal locations samples when (1)time since the last point of confusion is less than the maximum time to confusion (2)at the current time tracking uncertainty is above the uncertainty threshold

18. Reacquisition Tracking Model Time Window w=10Minutes. After the confusion Timeout expires: Each released sample need to maintain confusion from the last released positions within the window Before the confusion Timeout expires: Each released sample need to maintain confusion to any released samples within the windows

19. Evaluation: Data Set week-long GPS traces of 233 probe vehicles on a 70km-by-70km area 1 minute sampling period Overlay it into day-long traces of 2000 vehicles Metrics: Tracking time and (relative) weighted road coverage Baseline algorithm: random sampling with probability p

20. Evaluation: Protection Against Target Tracking- Bounded Tracking Time without Reacquisition Uncertainty-aware privacy algorithm limits time to confusion to 5 min while random sampling algorithm’s TTC is a lot longer Uncertainty-aware privacy algorithm can release up to 92.5% of the original location samples while random sampling has to remove more samples

21. Evaluation: Protection Against Target Tracking- Dependence on Reacquisition and Density TTC of uncertainty-aware privacy algorithm is shorter than subsampling algorithm

22. Evaluation: Protection Against Target Tracking In very low density scenarios, uncertainty-aware privacy algorithm preserves maximum TTC guarantee of 5 min by removing more samples while subsampling allows a longer maximum TTC

23. Evaluation: Quality of Service Analysis Achieves a relative weighted road coverage similar to that of original location traces

24. Conclusion & Future Work Conclusion: 1.Proposed time-to-confusion metric to characterize location privacy 2.Uncertainty-aware Path Cloaking outperforms existing algorithm in privacy protection in low density areas with good data quality Future Work 1.Adversary with a priori knowledge 2.Without a trustworthy location server 3.Track vehicles by speed information 4.Group of vehicles with the same starting point, destination and move together

25. Questions & Thoughts yl177@duke.edu