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Laurent Bindschaedler, Igor Bilogrevic,Jean-Pierre Hubaux (EPFL, Switzerland)

Track Me If You Can: On the Effectiveness of Context-based Identifier Changes in Deployed Mobile Network s (2012). Laurent Bindschaedler, Igor Bilogrevic,Jean-Pierre Hubaux (EPFL, Switzerland) Murtuza Jadliwala (Wichita State University, USA)

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Laurent Bindschaedler, Igor Bilogrevic,Jean-Pierre Hubaux (EPFL, Switzerland)

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  1. Track Me If You Can: On the Effectiveness of Context-based Identifier Changes in Deployed Mobile Networks (2012) Laurent Bindschaedler, Igor Bilogrevic,Jean-Pierre Hubaux (EPFL, Switzerland) Murtuza Jadliwala (Wichita State University, USA) Imad Aad, Philip Ginzboorg, Valtteri Niemi (nokia.com) Presented by Santiago Vera

  2. Presentation Outline • Introduction • The Goal • System Model • Data collection and Processing • Tracking Framework and Algorithms • Empirical Results and Evaluation • Conclusion

  3. Device-to-device Introduction

  4. Pervasive Communication Systems • Static identifiers Source:

  5. Pervasive Communication Systems • Static identifiers Source:

  6. Location Privacy is a critical issue.

  7. How the privacy can be protected? • Replace device identifiers with short live identifiers (pseudonyms) • Mix-zones: spatio-temporal regions where pseudonyms of users can change or mixed to provide de-correlation between pseudonyms and devices (Beresford and Stajano[9]) • TMSI Santiago Puppy Kitty Monkey

  8. The Goal • To Evaluate mix-zones and context-based identifier-change mechanisms by means of a real on-campus mobile network deployment.

  9. System Model • Mobile Network Model and Deployment http://conversations.nokia.com/2010/05/25/nokia-instant-community-gets-you-social/

  10. Nokia Instant Community (NIC) • Multi-hop P2P network based on IEEE 802.11 • Publish-subscribe messaging • Communities NIC trial • 80 volunteers • Students and staff in EPFL campus for 4 months. (2011) • Nokia N900 smartphones with NIC. • Log everything

  11. Pseudonym Change Algorithm (PCA) • Change pseudonym: context-based and at fixed intervals (random) - Mix-zones (regions, change identifiers) • Mix request, others users also change pseudonyms • To prevent network performance, limit # pseudonyms • MAC address Alice John Bob Mary

  12. PCA Parameters

  13. Adversary Model and Deployment • Passive • Eavesdrops by mesh network ( 37 wireless routers or APs) of sniffing stations • Weaker than Dolev-Yao model • No access to any device • Amount of data • Reconstruction attack

  14. Data collection and Processing

  15. Presentation Outline • Introduction • The Goal • System Model • Data collection and Processing • Tracking Framework and Algorithms • Empirical Results and Evaluation • Conclusion

  16. Tracking Model • Finite state first order Markov Chain • Where there is States S S= state space s=each state s=(pseudonym, first event, last event) • Transition probability P: S x S [0,1] (user invariant) - Validity - Time monotonicity

  17. Adversarial Tracking Strategies • L-WALK Perform a walk in the state space such that the next state candidate with the highest probability is selected at every step(the walk is locally optimal) • G-WALK Perform a walk in the state space such the probability over the entire walk is maximized over all walks. (the walk is globally optimal)

  18. To Estimate Transition Probabilities by using two heuristics • Common sniffing stations The higher # of common sniffing stations between the current state and the next state candidate, the higher the probability of transitioning. • Speed matching The closer the user speeds between the current state and the next state candidate, the more likely the candidate. (Speed between 2 events)

  19. Presentation Outline • Introduction • The Goal • System Model • Data collection and Processing • Tracking Framework and Algorithms • Empirical Results and Evaluation • Conclusion

  20. Privacy Metrics • Traceability metrics • Uncertainty metrics • Traceability-Uncertainty metrics • Clustering metrics

  21. Tracking Results

  22. Tracking Results

  23. Tracking with Adversary Strengths

  24. Tracking with Adversary Strengths

  25. Tracking with Aggressive PCA

  26. Tracking with Improved PCA in multiple user • PCA with radio silence randomized over a larger time interval • PCA with longer radio silence • PCA with radio silence until movement detected. Speed matching Common sniffing stations

  27. Conclusion • Even simple tracking strategies achieve high traceability success in real settings. • Pseudonym change reduces the tracking success of the adversary and has an impact on network performance. • A decrease in number of adversary sniffing stations results in lower traceability. • Find a generic adversary model weaker than Dolev-Yao model but stronger than localized and stationary eavesdropper.

  28. Thank You! Questions?

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