Compromising Location Privacy in Wireless Networks Using Sensors with Limited Information

1. Compromising Location Privacyin Wireless Networks Using Sensors with Limited Information Author: Ye Zhu and Riccardo Bettati Department of computer science, Texas A&M University Presenter: Kai-shin Lu

2. The Problem • How to find out the positions of fixed wireless nodes?

3. Naïve Solution 1 • He tells me (eavesdrop) I am in Atanosoff. I want to order one pizza. If I can protect my position information? (e.g. cloaking, encrypting) If I don’t need any location service?

4. Naïve Solution 2 • Directive sender If I don't’ have enough money to buy directive sensors?

5. Problem • How to compromising location privacy in wireless networks using sensors with Limited Information ?

6. Solution • Step 1. Deploy sensors (spies) among wireless nodes to eavesdrop data • We know the position of deployed sensors Nodes Nodes + Sensors

7. Solution • Step 1. (Continue) • The sensors only collect the time series of packet counts • E.g. [100,200,13] I got 100 packets during 0-10 seconds. I got 200 packets during 11-20 seconds. I got 13 packets during the next 10 seconds. Control center

8. Solution • Step 2. Use Principal Component Analysis (PCA) to estimate node numbers in this area

9. 531 grade 511 grade Principal Component Analysis (PCA) • An important statistics technique The second component 531 grade Mike IQ 511 grade The first (principal) component

10. Principal Component Analysis (PCA) • This skill can be applied to 3 or more dimensional data

11. What can we do with PCA? • Suppose we draw a point for a time period... The red point represents the 3rd time period’s data. Its coordinate is (13,8,6) [x,x,8,x,x,…] Packet # of Sensor 2 6 13 8 Packet # of Sensor 1 [x,x,13,x,x,…] Packet # of Sensor 3 [x,x,6,x,x,…]

12. What can we do with PCA? • Draw all points • Is there any hidden factor behind these data? Yes! There are 2 hidden factors which greatly affect the data !! There are 2 wireless nodes in this area !!

13. Solution • Step 2. Use Principal Component Analysis (PCA) to estimate node numbers in this area • Step 3. Then use Blind Source Separation(BSS) to estimate the positions of nodes

14. Blind Source Separation (BSS) • BSS was originally developed to solve the cocktail party problem • Which can extract one person’s voice signal given a mixtures of voices at a cocktail party Hi Mike, how are you doing today? …So I went to HyVee yesterday.

15. Nice property of BSS • Get unmixed singles from mixed signals • Suppose sensor 1 got : [5, 0, 1, 0, 1 ] • Apply BSS, we can get unmixed signals • One is [3,0,0,0,0] – which might come from Node A • One is [2,0,0,0,1] – which might come from Node B • One is [0,0,1,0,0] – which might be noise Sensor 1 Node B Node A

16. What can we do with BSS? • Trick: We cut the whole area into many overlapped blocks 1

17. What can we do with BSS? • Trick: We cut the whole area into many overlapped blocks 2

18. What can we do with BSS? • Trick: We cut the whole area into many overlapped blocks 3

19. What can we do with BSS? • Trick: We cut the whole area into many overlapped blocks 4

20. What can we do with BSS? • Trick: We cut the whole area into many overlapped blocks This square belongs to 4 blocks

21. What can we do with BSS? • For each block, we apply BSS to get many separated signals [How are you]

22. What can we do with BSS? • For each block, we apply BSS to get many separated signals [How or you] {Cab sin} [How are you]

23. What can we do with BSS? • For each block, we apply BSS to get many separated signals [How or you] {Cab sin} [How are you] [How are youth]

24. What can we do with BSS? • For each block, we apply BSS to get many separated signals [How or you] {Cab sin} [How are you] [haha you] [How are youth]

25. Cluster 1 noise, ignore What can we do with BSS? • Cluster the separated signals together based on similarity [How or you] {Cab sin} [How are you] [haha you] [How are youth]

26. What can we do with BSS? • By analyzing the overlap of signals, we can estimate the position of them. [How are you]

27. Solution summary • By PCA, we know that there are n nodes • Cut whole area into many overlapped blocks • Apply BSS in each block • Get many separated (unmixed) signals • Cluster them together based on similarity • Pick up n largest clusters • Use overlap analysis to estimate the positions of nodes

28. Discuss • Good: • If the nodes are fixed, then it provides a cheap way to get their positions even though the data are perfectly encrypted • Bad: • The nodes should be fixed • If nodes can manipulate signal power, the overlap analysis part will fail • It assume that the communications among sensors won’t affect normal data collecting

29. Q & A