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O n the Relative De-anonymizability of Graph Data: Quantification and Evaluation Shouling Ji, Weiqing Li, Shukun Yang and Raheem Beyah Georgia Institute.

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Presentation on theme: "O n the Relative De-anonymizability of Graph Data: Quantification and Evaluation Shouling Ji, Weiqing Li, Shukun Yang and Raheem Beyah Georgia Institute."— Presentation transcript:

1 O n the Relative De-anonymizability of Graph Data: Quantification and Evaluation Shouling Ji, Weiqing Li, Shukun Yang and Raheem Beyah Georgia Institute of Technology Prateek Mittal Princeton University

2 S. Ji, W. Li, S. Yang, P. Mittal, and R. BeyahOn the Relative De-anonymizability of Graph Data 2 Big Graph Data Definition – Volume – Structure – Semantics – …… Existing Big Graph Data – Social networks data – Mobility traces – Medical data – ……

3 S. Ji, W. Li, S. Yang, P. Mittal, and R. BeyahOn the Relative De-anonymizability of Graph Data 3 Graph Data Sharing/Publishing Academic Research Government Applications Business Applications Healthcare Applications Other Scenarios – Release data by law – Online crawling – Data sharing websites – Data Brokers

4 S. Ji, W. Li, S. Yang, P. Mittal, and R. BeyahOn the Relative De-anonymizability of Graph Data 4 Anonymization and De-anonymization Anonymization De-anonymization Users’ records Movie view history Political preferences Physical size …… Religious views Sexual orientation Passwords Location ……

5 S. Ji, W. Li, S. Yang, P. Mittal, and R. BeyahOn the Relative De-anonymizability of Graph Data 5 Research Picture SecGraph: Secure Graph Data Publishing/Sharing (USENIX Security’15) Theoretical Foundation Seed-based De-anonymizability Quantification (NDSS’15) Seed-free De-anonymizability Quantification (CCS’14) Structure Importance-aware De-anonymizability Quantification ……. New De-anonymization Attacks DeA, ADA (ISC’14) ODA (CCS’14) De-SAG ……… Defense k -anonymity Differential Privacy …… Utility Graph Utility Application Utility ……

6 S. Ji, W. Li, S. Yang, P. Mittal, and R. BeyahOn the Relative De-anonymizability of Graph Data 6 Structure Importance-aware Quantification Motivation and Contribution Summarization Seed-based Relative De-anonymization (SBRD) Quantification Seed-free Relative De-anonymization (SFRD) Quantification Both Perfect and Error-tolerant Scenarios Large-scale Evaluation on Real World Graph Data (15 datasets) Evaluation of State-of-the-Art Quantifications

7 S. Ji, W. Li, S. Yang, P. Mittal, and R. BeyahOn the Relative De-anonymizability of Graph Data 7 Outline Introduction Quantification Large-scale Evaluation Discussion Conclusion

8 S. Ji, W. Li, S. Yang, P. Mittal, and R. BeyahOn the Relative De-anonymizability of Graph Data 8 Quantification Data model – Configuration model

9 S. Ji, W. Li, S. Yang, P. Mittal, and R. BeyahOn the Relative De-anonymizability of Graph Data 9 Quantification Data model – Configuration model De-anonymization – User mapping

10 S. Ji, W. Li, S. Yang, P. Mittal, and R. BeyahOn the Relative De-anonymizability of Graph Data 10 Quantification Data model – Configuration model De-anonymization – User mapping Main technique – Edge Error function – Take the mapping that can minimize the edge error function as the final de- anonymization result

11 S. Ji, W. Li, S. Yang, P. Mittal, and R. BeyahOn the Relative De-anonymizability of Graph Data 11 Quantification Data model – Configuration model De-anonymization – User mapping Main technique – Edge Error function Quantification – Seed-based – Seed-free

12 S. Ji, W. Li, S. Yang, P. Mittal, and R. BeyahOn the Relative De-anonymizability of Graph Data 12 Outline Introduction Quantification Large-scale Evaluation Discussion Conclusion

13 S. Ji, W. Li, S. Yang, P. Mittal, and R. BeyahOn the Relative De-anonymizability of Graph Data 13 Datasets

14 S. Ji, W. Li, S. Yang, P. Mittal, and R. BeyahOn the Relative De-anonymizability of Graph Data 14 Seed-based Evaluation Anonymized GraphAuxiliary Graph Seed mappings

15 S. Ji, W. Li, S. Yang, P. Mittal, and R. BeyahOn the Relative De-anonymizability of Graph Data 15 Seed-based Evaluation Target the top 50% users with respect to user degree # of seed users Measure the structural similarity between the anonymized graph and the auxiliary graph How many users can be successfully de-anonymized

16 S. Ji, W. Li, S. Yang, P. Mittal, and R. BeyahOn the Relative De-anonymizability of Graph Data 16 Seed-free Evaluation Target the top 50% users with respect to user degree Measure the structural similarity between the anonymized graph and the auxiliary graph How many users can be successfully de-anonymized

17 S. Ji, W. Li, S. Yang, P. Mittal, and R. BeyahOn the Relative De-anonymizability of Graph Data 17 State-or-the-Arts SBD: Seed-based De-anonymization Quantification SBRD: Seed-based Relative De-anonymization Quantification SFD: Seed-free De-anonymization Quantification SFRD: Seed-free Relative De-anonymization Quantification

18 S. Ji, W. Li, S. Yang, P. Mittal, and R. BeyahOn the Relative De-anonymizability of Graph Data 18 Outline Introduction Quantification Large-scale Evaluation Discussion Conclusion

19 S. Ji, W. Li, S. Yang, P. Mittal, and R. BeyahOn the Relative De-anonymizability of Graph Data 19 Discussion Practicality Accuracy Generality Future work

20 S. Ji, W. Li, S. Yang, P. Mittal, and R. BeyahOn the Relative De-anonymizability of Graph Data 20 SecGraph: Release & Support Website – http://www.ece.gatech.edu/cap/secgraph/ http://www.ece.gatech.edu/cap/secgraph/ – Software – Datasets – Documents – Demo – Q&A Modes – GUI – Command line Supporting – Windows – Linux – Mac

21 S. Ji, W. Li, S. Yang, P. Mittal, and R. BeyahOn the Relative De-anonymizability of Graph Data 21 Conclusion – Seed-based Relative De-anonymizability – Seed-free Relative De-anonymizability – Large Scale Evaluation

22 S. Ji, W. Li, S. Yang, P. Mittal, and R. BeyahOn the Relative De-anonymizability of Graph Data 22 Thank you! Shouling Ji sji@gatech.edu http://www.ece.gatech.edu/cap/secgraph/ http://users.ece.gatech.edu/sji/

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