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PowerSpy: Location Tracking using Mobile Device Power Analysis Security paper, USENIX 2015, Dan Boneh
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3:国家专业人才认证中心颁发入库证书
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- 1. PowerSpy: Location Tracking using Mobile Device Power Analysis Master’s course 29th Park Joon-young
- 2. Contents • Working idea • Paper introduction • Techniques & Evaluation • Conclusion of paper • What I have to do
- 3. Paper introduction • PowerSpy, Dan Boneh • USENIX Security, 2015 / Acceptance ratio : 15.7%(67/426) • Learn user’s location from reading phone’s power consumption
- 4. Contributions • Showed power meter available on medern phones can reveal potentially private information • Developed the machine learning techniques for infer location information from power meter data. • Discussed potential continuation, countermeasures for this work.
- 5. Assumptions • Malicious application is installed and running in background • Only has permission to access power data, network communication - Cannot measure the power consumed by the cellular radio alone. • Prior knowledge of the area/routes through which the victim moves. • Cannot locate a phone that is standing still.
- 6. Motivation
- 7. Motivation
- 8. Motivation
- 9. Motivation
- 10. Motivation
- 11. Motivation
- 12. Motivation
- 13. Motivation Signal strength affects power consumption
- 14. Motivation
- 15. Motivation Same model Defferent model
- 16. • And we can get battery info without any permissions Motivation
- 17. Challenges • Pre-measurement may have different speed or stops. • Have to identify the target’s power profile in many pre-collected profiles along different routes • Exact location of the target may be ambiguous • Target may travel the road which the attacker partially measured.
- 18. Route distinguishing • Each power profile is time series • used Dynamic Time Warping
- 19. Euclidean DTW Route distinguishing - Dynamic Time Warping
- 20. Route distinguishing • Normalization • MA Smoothing (obtain general trends) • Downsampling (computation reduction)
- 21. Real-time tracking • Track target in real-time • Subsequence DTW • Motion model applied • Optimal Subsequence Bijection
- 22. OSB DTW - Optimal Subsequence Bijection Real-time tracking
- 23. Real-time tracking - Optimal Subsequence Bijection
- 24. Inference of new routes Particle Filter
- 25. Particle Filter
- 26. Particle Filter
- 27. Particle Filter
- 28. Particle Filter
- 29. Particle Filter
- 30. Particle Filter
- 31. Particle Filter
- 32. Particle Filter
- 33. Particle Filter
- 34. Evaluation • Nexus 4, Nexus 5, HTC • Enough communication is occuring • Driver might be using nav sw or streaming music • Unexpected events (e.g., phone call) can be normalized using MA
- 36. - Real-time tracking Evaluation Before & After using improved motion model
- 37. Conclusion Without location based permission, Route identification Real-time tracking Identify new route
- 38. Question?