1. VoteTrustLeveraging Friend Invitation Graph to DefendSocial Network Sybils Jilong Xue , Zhi Yang , Xiaoyong Yang, Xiao Wang, Lijiang Chen and Yafei Dai Computer Science Department, Peking University

2. Sybil attack in Social networks Sybils Friend invitation reject Non-popular users accept

3. VoteTrust: An Overview • Basic idea: • Considering invitation feedback as voting • Key techniques: • Trust-based votes assignment • Global vote aggregation • Properties: • High precision in Sybil detection • Efficient in limiting Sybil’s attack ability

4. Graph Model A A 1 C C 0 B B Link initiation graph Link acceptance graph

5. Framework of VoteTrust • Select trust seed – high reliable users • Distribute votes • Collect votes and computing score

6. Outline • Preliminary • Implementation • Trust-based vote assignment • Global vote aggregation • Evaluation • Conclusion

7. Votes Assignment • Problem: • How to distribute votesacross users? • Principle: • Reliable user should get more votes • How to implement? v Reliable user v v v v Non-popular user Sybil

8. Trust-based Votes Assignment • Step1: Assigning votes to little human-selected reliable seeds • Step2: Propagating to whole users across the Link initiation graph

9. Example A B C D E A B t=0 5 0 0 0 0 t=1 0.75 4.25 0 0 0 t=2 0.75 0.65 0 1.80 1.80 C D E t=3 2.59 0.94 0.31 0.58 0.58 … t=n 1.69 1.57 0.14 0.80 0.80 • Node A is reliable seed • Total votes =5

10. Outline • Preliminary • Implementation • Trust-based vote assignment • Global vote aggregation • Evaluation • Conclusion

11. Vote Aggregating • Problem: • How to collect votes and compute user trust score? • Trust score • Principle: • Trust user should have high weight in voting. vote=1,score=0.2 vote=1,score=0.9 A B 0 1 C score=?

12. Global Vote Aggregation • Step1: Set all users’ initial score as 0.5; • Step2: Iteratively computing each user’s trust score according to aggregated votes.

13. Small-sample Problem • Number of votes is too small. • Wilson score • weighted average of and . vote=1,score=0.2 0 score=0 ? A B score=0.40 vote=1,score=0.2 1 score=1 ? A B score=0.61

14. Security Properties (I) • Theorem 1:The number of Sybil’s attack-link needs to satisfy the following upper bound where is detection threshold. Sybil Normal user

15. Simulation of Theorem 1 • Comm size: 100 • # of in-links: 10 • Noutavg: 2.36 • Noutmax:4

16. Security Properties (II) • Theorem 2: Sybil community size need to satisfy the upper bound , where is vote collection threshold.

17. Simulation of Theorem 2

18. Outline • Preliminary • Implementation • Trust-based vote assignment • Global vote aggregation • Evaluation • Conclusion

19. Experimental Setup • Data Set • Renren regional network (PKU) include 200K users, 5.01 million friend invitations • 2502 Sybil accounts detected by Renren • Manual checking 73 Sybils from 500 random user • Methodology • Compared with TrustRank and BadRank • Evaluation metrics: Precisionand Recall

20. TrustRank vs. VoteTrust Averagely improve 32.9% Averagely improve 75.6%

21. BadRank vs. VoteTrust Averagely improve 44.5% Averagely improve 41.6%

22. Separating Normal User from Sybils 80% with low score

23. Separating Normal User from Sybils Maximum accuracy=85.7%

24. Performance Summary • Outperforms TrustRank by 32.9% in detection precision averagely; • Outperforms BadRank by 44.5% in detection precision averagely; • High accurate in classifying the Sybil and normal user (include non-popular user)

25. Outline • Preliminary • Implementation • Trust-based vote assignment • Global vote aggregation • Evaluation • Conclusion

26. Conclusion • VoteTrust is a rating system • high accuracy in Sybil detection • Efficient in resisting Sybil (community) • Key techniques • Trust-based vote assignment • Global vote aggregation

27. Thank you!