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Evaluating LBS Privacy In DYNAMIC CONTEXT

Evaluating LBS Privacy In DYNAMIC CONTEXT. Implement report (12/05/2011). Outline. Architecture Implement Merge module Algorithm module Reciprocity module Experiment Conclusion. Outline. Architecture Implement Merge module Algorithm module Reciprocity module Experiment

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Evaluating LBS Privacy In DYNAMIC CONTEXT

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  1. Evaluating LBS Privacy In DYNAMIC CONTEXT Implement report (12/05/2011)

  2. Outline • Architecture • Implement • Merge module • Algorithm module • Reciprocity module • Experiment • Conclusion

  3. Outline • Architecture • Implement • Merge module • Algorithm module • Reciprocity module • Experiment • Conclusion

  4. Outline • Architecture • Implement • Merge module • Algorithm module • Reciprocity module • Experiment • Conclusion

  5. Outline • Architecture • Implement • Merge module • Algorithm module • Reciprocity module • Experiment • Conclusion

  6. Implemented algorithms • Nearest-neighbor ASR (nnASR) • R-Tree Index • Different results when run many times with same input • Interval Cloaking • Quad-Tree index • Same input – same result • Grid • Sorted List • Same input – same result

  7. Outline • Architecture • Implement • Merge module • Algorithm module • Reciprocity module • Experiment • Conclusion

  8. Checking reciprocity module • Input: issuer id + MBR • Output: number of users which have same MBR – real k • Algorithm: • Set k_anonymity = 0 • Run anonymizing algorithm to get AS • For each id ui in AS, run algorithm to get ASi • If AS = ASi then k_anonymity = k_anonymity + 1 • If k_anonymity >= k, return true • Else return false

  9. Problem with reciprocity property • An assumption about anonymizing algorithm: • In snapshot, same input  same result • Problem • Algorithm: same input  different results • Example: nnASR I I 1st time 2nd time

  10. nnASR: an attack proposed • Assumption • K is known • Idea • Find the chosen users • Its k-nn must be in or be the original MBR • Forecast the candidate issuer • For each user in original MBR (exclude chosen users) • Check whether its k-nninclude one of chosen users & expand MBR is equal to original MBR • True  candidate

  11. Illustration • k = 4 Candidate

  12. Illustration • k = 4 Candidate

  13. Refine algorithm • Just refine value k of request • Brute-force: • Increase k until request satisfies reciprocity property • Suitable for algorithms which: • Same input  same result • Problem: • nnASR

  14. Outline • Architecture • Implement • Merge module • Algorithm module • Reciprocity module • Experiment • Conclusion

  15. Experiment • Implement the algorithms in Java • System configuration: • OS: Window 7 • Processor: AMD Phenom II X4 B40 3.0Ghz • RAM: 2GB • Data: users’ locations in Sanfrancisco with 17000 users • Run 500 tests and take the average to get output values

  16. Experiment

  17. Experiment Average size of the generalized region

  18. Experiment

  19. Conclusion • nnASRalgorithm: how to improve privacy of user? • The performance of reciprocity module

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