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

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

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

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 )

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

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

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

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)

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

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

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

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

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

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

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

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

Simulation of Theorem 2

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

Experimental Setup Data Set Methodology 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: Precision and Recall

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

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

Separating Normal User from Sybils 80% with low score

Separating Normal User from Sybils Maximum accuracy=85.7%

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)

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

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

Thank you!