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Evaluating LBS Privacy in Dynamic Context: Implementation Report

This report evaluates the privacy aspects of Location-Based Services (LBS) in dynamic contexts, focusing on the architecture, implementation, algorithm modules, reciprocity module, and experimental results. It discusses challenges with the reciprocity property and proposes refinement algorithms to enhance user privacy. The experiment details the Java-based implementation and system configuration used, providing insights on improving user privacy with the nnASR algorithm and reciprocity module performance.

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Evaluating LBS Privacy in Dynamic Context: Implementation Report

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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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