1. A Unified Framework for Location Privacy
Shokri, R., Freudiger, J., & Hubaux, J. P. (2010).
3rd Hot Topics in Privacy Enhancing Technologies (HotPETs),
Paper Presentation
by George Corser
Oakland University , June 6, 2014

2. Abstract
Source: Shokri, 2010, page 1
Link to paper on Google Scholar

3. Outline of Paper Introduction Mobile Networks
Location Privacy Preserving Mechanisms
Threat Model*
Location Privacy Measurement
Application Scenario: LBS
Related Work
Conclusion
* Defines structure of a threat model, not a particular threat model
LBS: Location Based Service

4. 1. Introduction: Definition
Source: Shokri, 2010, page 1

5. 1. Introduction: Problem
Source: Shokri, 2010, page 1

6. 1. Introduction: Solution
Source: Shokri, 2010, page 2

7. 2. Mobile Networks: App. Categories
Set of mobile users with wireless devices
Connections ad hoc or via cellular, wifi, ...
Applications like LBS, mobile social network, recommender systems, friend finder, ...
Automatic
Manual
Continuous
People Sensing
LBS (w/FPL*)
Discrete
Elec. Ticketing
LBS (typical)
Source: Shokri, 2010, pages 2-3
LBS: Location Based Service, FPL: Frequent Precise Location

8. 2. Mobile Networks: Events
Users ( ): set of mobile network members
Each has real identity ( ) and pseudonym ( )
Time and Space
Time instance (t): a natural number in time period
Location instance ( ): a coordinate on a grid of
The Spatiotemporal State of Users (Events)
Event: a 3-tuple < identity, time, location >
Actual trajectory, all events of user u ( ):
Source: Shokri, 2010, pages 3-5

9. 3. Privacy Preserving Mechanisms
Distributed (user-side): Adding noise to GPS coordinates
Centralized (server-side): Cloaking proxy server
Hybrid
Source: Shokri, 2010, pages 5-7
GPS: Global Positioning System

10. 4. Threat Model
Source: Shokri, 2010, pages 7-11

11. 5. Location Privacy Measurement
Microscopic: within a time interval
Uncertainty: size of anonymity set of positions
Macroscopic: throughout trajectory
Uncertainty: size of anonymity set of trajectories
Error-based metrics
Clustering-error metrics (degree of path confusion)
Probability of error metrics (degree of accuracy in using endpoint map deanonymization)
Distortion-based metrics (geographical precision)
Consider user location privacy (probability of error) versus system location privacy (uncertainty/entropy)
Source: Shokri, 2010, pages 11-14
Map deanonymization: Linking identities with endpoint home/work locations

12. 6. Application Scenario: LBS
Application is manual and discrete
Entity: User
Anonymization when connect with pseudonym
Hiding when not connected to LBS
Entity: Application
Obfuscation when GPS not precise
Entity: Privacy Tool
Dummification when using group pseudonym
If FPL, application may be manual and continuous
Source: Shokri, 2010, pages 14
LBS: Location Based Service, FPL: Frequent Precise Location
Highly effective in centralized architecture

13. 6. App. Scenario: LBS: Threat Model
Means
Access
Server side application
Knowledge
Map deanonymization
Actions
Type
Passive
Spatial Scope
Global
Temporal Scope
Long-term
Goals
Disclosure
Presence
Target
Individual
Attack
Identification
Tracking
Source: Shokri, 2010, pages 14-15

14. 6. App. Scenario: LBS: Open Problems
Source: Shokri, 2010, pages 15

15. 7. Related Work Hong/Landay [43]: toolkit (Confab)
Duckham/Kulik [26]: obfuscation mechanisms
Bettini et al [11]: microscopic location privacy
Decker [24]: LBS location privacy problems
Blumberg/Eckersley [13]: emerging threats
Krumm [52]: literature review
Shokri [66]: macroscopic location privacy
Source: Shokri, 2010, pages 15-16

16. 8. Conclusion
Source: Shokri, 2010, pages 16