Protecting Location Privacy Though Path Confusion [1]

1. Protecting Location Privacy Though Path Confusion [1] Baik Hoh, Marco Gruteser CS898 Presentation By Jason Tomlinson

2. Introduction • A quick overview of this papers primary purpose. • Outline for our Discussion • Two types of location based technologies • Location data and evolving applications • A need for privacy • The path perturbation algorithm and QoS (Quality of Service) constraints

3. Quick Overview • The accuracy of location based information is improving • Applications continuously collect based location information • There is a need to preserve privacy while maintaining quality of service • Removing identifiable information is not sufficient • Privacy through path perturbation given a QoS Constraint

4. Types of Location Services • There are two primary types • GPS • Cellular • Others • Accuracy and availability continues to improve

5. Applications of Location Services • What level of information is needed to provide utility? • Transportation Support Applications • Transportation planning • Rout Planning • Road conditions • Traffic Analysis • Alternate Routes • Other Types of applications

6. The Need for Location Privacy • **The Location Privacy Act of 2001 and the Wireless Protection act of 2003 • Why Stripping identifiable information is not sufficient? • Location tracking patterns: temporal and spatial correlation • MTT Multi-Target Tracking • What kind of private information can we extract? • What implications can be made?

7. Association of Location to Users

8. Metrics • Entropy based metrics are often used to evaluate privacy. • Formula: • Pi = Adversary Probabilities for assignment of different user identities to positions • I = Total number of assignment hypotheses Note 1: The degree of privacy is a measure of the accuracy with which and Adversary can locate an individual user. Note 2: Entropy does not consider whether the locations of two users are different

9. Metrics • Alternate metric: expectation of distance error. • di = total distance error between correct assignment hypotheses and hypotheses i. • Pi = Adversary Probabilities for assignment of different user identities to positions • K = total observation time • N = number of users Note 1: Captures how accurate an adversary can estimate a user’s position

10. Metrics • Data quality for application services depends on accuracy of location information. • Mean location error • xn(k), yn(k) represent the actual location • xn (k), yn (k) is the observed location of user n at step k

11. The Path Perturbation Algorithm • Key Idea: Leverage two user close proximity to confuse the adversary • Increase the probability of confusion using a perturbation algorithm. • Cost Function: • R = User or Application input specifying Maximum allowed perturbation

12. The Path Perturbation Algorithm • The goal is to maximize distance error • pi = distance error • di = adversary’s probability

13. The Path Perturbation Algorithm

14. Path Segmentation

15. Summary • A quick overview of this papers primary purpose. • Two types of location based technologies • Location data and evolving applications • A need for privacy • The path perturbation algorithm and QoS (Quality of Service) constraints

16. References • [1] “Protecting Location Privacy through Path Confusion”, Baik Hoh and Marco Gruteser, SecureComm, 2005.

17. The Path Perturbation Algorithm • Reference for Gaussian Density Formulas based on MTT

18. The Path Perturbation Algorithm • Reference Formulas:

19. The Path Perturbation Algorithm

20. The Path Perturbation Algorithm

21. The Path Perturbation Algorithm