1. Computer Science Department, Peking University
VoteTrust Leveraging Friend Invitation Graph to Defend Social 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
Non-popular users
reject
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 B B Link initiation graph 𝐺 𝐼 =( 𝑉 𝐼 , 𝐸 𝐼 )B B
Link initiation graph 𝐺 𝐼 =( 𝑉 𝐼 , 𝐸 𝐼 )
Link acceptance graph 𝐺 𝐸 =( 𝑉 𝐸 , 𝐸 𝐸 , 𝑊 𝐸 )

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

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

7. Votes Assignment Problem: Principle: How to implement?
How to distribute votes across users?
Principle:
Reliable user should get more votes
How to implement? v v
Reliable user 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
𝒗𝒐𝒕𝒆(𝑢)=𝑑∙ 𝑣:(𝑣,𝑢)∈ 𝐸 𝐼 𝒗𝒐𝒕𝒆(𝑣) 𝑜𝑢𝑡_𝑑𝑒𝑔𝑟𝑒𝑒(𝑣) + 1−𝑑 ∙𝒊𝒏𝒊𝒕(𝑢)

9. Example … A B C D E A B C D E Node A is reliable seed Total votes =5
t=1
0.75
4.25
t=2
0.75
0.65
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 Evaluation Conclusion
Trust-based vote assignment
Global vote aggregation
Evaluation
Conclusion

11. Vote Aggregating Problem: Principle:
How to collect votes and compute user trust score?
Trust score 𝑝 𝑢 ∈[0,1]
Principle:
Trust user should have high weight in voting.
vote=1,score=0.2
vote=1,score=0.9 A B 1 C
score=?
𝑠𝑐𝑜𝑟𝑒(𝐶)= 1×0.9 1×0.2+1×0.9 =0.82

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.
𝒔𝒄𝒐𝒓𝒆 𝑢 = 𝑣𝑜𝑡𝑒 𝑣 ∙𝒔𝒄𝒐𝒓𝒆 𝑣 ∙ 𝑥 𝑣,𝑢 𝑣𝑜𝑡𝑒 𝑣 ∙𝒔𝒄𝒐𝒓𝒆 𝑣 , (𝑣,𝑢)∈ 𝐸 𝐸

13. Small-sample Problem Number of votes is too small. Wilson score
weighted average of 𝒑 and
vote=1,score=0.2 A B
score=0 ?
score=0.40
𝒑= 𝒑 + 1 2𝑁 𝑧 1− 𝛼 𝑁 𝑧 1− 𝛼 2
vote=1,score=0.2 A 1 B
score=1 ?
score=0.61

14. Security Properties (I)
Theorem 1: The number of Sybil’s attack-link needs to satisfy the following upper bound
where 𝛿 𝑓 is detection threshold.
𝑵 𝒐𝒖𝒕 ≤𝜌 𝑵 𝒊𝒏 ∙ 𝛿 𝑓 − 𝛿 𝑓 2 𝛿 𝑓 −𝑟
𝑁 𝑖𝑛
Normal user
Sybil
𝑁 𝑜𝑢𝑡

15. Simulation of Theorem 1 Comm size: 100 # of in-links: 10
Nout avg: 2.36
Nout max:4

16. Security Properties (II)
Theorem 2: Sybil community size need to satisfy the upper bound ,
where 𝛿 𝑣 is vote collection threshold.
𝑵 𝒔 ≤𝜎∙ 𝑵 𝒊𝒏 𝛿 𝑣

17. Simulation of Theorem 2

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

19. Experimental Setup Data Set Methodology
Renren regional network (PKU) include 200K users, 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: Precision and 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 Evaluation Conclusion
Trust-based vote assignment
Global vote aggregation
Evaluation
Conclusion

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

27. Thank you!