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Structural Data De-anonymization: Quantification, Practice, and Implications Shouling Ji, Weiqing Li, and Raheem Beyah Georgia Institute of Technology.

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Presentation on theme: "Structural Data De-anonymization: Quantification, Practice, and Implications Shouling Ji, Weiqing Li, and Raheem Beyah Georgia Institute of Technology."— Presentation transcript:

1 Structural Data De-anonymization: Quantification, Practice, and Implications Shouling Ji, Weiqing Li, and Raheem Beyah Georgia Institute of Technology Mudhakar Srivatsa IBM T. J. Watson Research Center

2 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization Narayanan-Shmatikov attack (IEEE S&P 2009) A. Narayanan and V. Shmatikov, De-anonymizing Social Networks, IEEE S&P 2009. Anonymized data: Twitter (crawled in late 2007) – A microblogging service – 224K users, 8.5M edges Auxiliary data: Flicker (crawled in late 2007/early 2008) – A photo-sharing service – 3.3M users, 53M edges Result: 30.8% of the users are successfully de-anonymized TwitterFlicker User mapping Heuristics Eccentricity Edge directionality Node degree Revisiting nodes Reverse match

3 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization Srivatsa-Hicks Attacks (ACM CCS 2012) M. Srivatsa and M. Hicks, De-anonymizing Mobility Traces: using Social Networks as a Side- Channel, ACM CCS 2012. Anonymized data – Mobility traces: St Andrews, Smallblue, and Infocom 2006 Auxiliary data – Social networks: Facebook, and DBLP Over 80% users can be successfully de-anonymized

4 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization Motivation Question 1: Why can structural data be de-anonymized? Question 2: What are the conditions for successful data de-anonymization? Question 3: What portion of users can be de-anonymized in a structural dataset? [1] P. Pedarsani and M. Grossglauser, On the Privacy of Anonymized Networks, KDD 2011. [2] L. Yartseva and M. Grossglauser, On the Performance of Percolation Graph Matching, COSN 2013. [3] N. Korula and S. Lattanzi, An Efficient Reconciliation Algorithm for Social Networks, VLDB 2014.

5 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization Motivation Question 1: Why can structural data be de-anonymized? Question 2: What are the conditions for successful data de-anonymization? Question 3: What portion of users can be de-anonymized in a structural dataset? Our Constribution Address the above three open questions under a practical data model.

6 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization Outline Introduction and Motivation System Model De-anonymization Quantification Evaluation Implication 1: Optimization based De-anonymization (ODA) Practice Implication 2: Secure Data Publishing Conclusion

7 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization System Model Anonymized Data Auxiliary Data De-anonymization Measurement

8 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization System Model Anonymized Data Auxiliary Data De-anonymization Measurement Quantification conceptual underlying graph Configuration Model G can have an arbitrary degree sequence that follows any distribution

9 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization Outline Introduction and Motivation System Model De-anonymization Quantification Evaluation Implication 1: Optimization based De-anonymization (ODA) Practice Implication 2: Secure Data Publishing Conclusion

10 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization De-anonymization Quantification Perfect De-anonymization Quantification Structural Similarity ConditionGraph/Data Size Condition

11 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization De-anonymization Quantification -Perfect De-anonymization Quantification Structural Similarity ConditionGraph/Data Size Condition

12 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization Outline Introduction and Motivation System Model De-anonymization Quantification Evaluation Implication 1: Optimization based De-anonymization (ODA) Practice Implication 2: Secure Data Publishing Conclusion

13 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization Evaluation Datasets

14 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization Evaluation Perfect De-anonymization Condition Structural Similarity Condition Graph/Data Size Condition

15 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization Evaluation -Perfect De-anonymization Condition Structural Similarity Condition Graph/Data Size Condition Projection/Sampling Condition

16 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization Evaluation -Perfect De-anonymizability Structural Similarity Condition

17 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization Evaluation -Perfect De-anonymizability Structural Similarity Condition How many users can be successfully de-anonymized

18 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization Outline Introduction and Motivation System Model De-anonymization Quantification Evaluation Implication 1: Optimization based De-anonymization (ODA) Practice Implication 2: Secure Data Publishing Conclusion

19 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization Optimization based De-anonymization (ODA) Our quantification implies – An optimum de-anonymization solution exists – However, it is difficult to find it. Select candidate users from unmapped users with top degrees Mapping candidate users by minimizing the Edge Error function

20 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization Optimization based De-anonymization (ODA) Our quantification implies – An optimum de-anonymization solution exists – However, it is difficult to find it. Space complexity Time complexity ODA Features 1. Cold start (seed-free) 2. Can be used by other attacks for landmark (seed) identification 3. Optimization based

21 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization ODA Evaluation Dataset – Google+ (4.7M users, 90.8M edges): using random sampling to get anonymized graphs and auxiliary graphs – Gowalla: Anonymized graphs: constructed based on 6.4M check-ins generated by.2M users Auxiliary graph: the Gowalla social graph of the.2 users (1M edges) – Results: landmark identification

22 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization ODA Evaluation Dataset – Google+ (4.7M users, 90.8M edges): using random sampling to get anonymized graphs and auxiliary graphs – Gowalla: Anonymized graphs: constructed based on 6.4M check-ins generated by.2M users Auxiliary graph: the Gowalla social graph of the.2 users (1M edges) – Results: de-anonymization

23 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization Outline Introduction and Motivation System Model De-anonymization Quantification Evaluation Implication 1: Optimization based De-anonymization (ODA) Practice Implication 2: Secure Data Publishing Conclusion

24 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization Secure Structural Data Publishing Structural information is important Based on our quantification – Secure structural data publishing is difficult, at least theoretically Open problem …

25 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization Conclusion – We proposed the first quantification framework for structural data de- anonymization under a practical data model – We conducted a large-scale de-anonymizability evaluation of 26 real world structural datasets – We designed a cold-start optimization-based de-anonymization algorithm Acknowledgement We thank the anonymous reviewers very much for their valuable comments!

26 S. Ji, W. Li, M. Srivatsa and R. BeyahStructural Data De-anonymization Thank you! Shouling Ji sji@gatech.edu http://users.ece.gatech.edu/sji/

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