PowerSpy: Location Tracking using Mobile Device Power Analysis Security paper, USENIX 2015, Dan Boneh

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

35. Evaluation
- Route distinguishing

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?