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Reputation Systems II

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Yury Lifshits
Yury Lifshits

The document discusses several algorithms for reputation and ranking systems on graphs, including blogs and social networks. It begins with discussing the Sybil attack and SybilGuard for detecting such attacks. It then covers algorithms for ranking blogs like BlogRank, B2Rank, and EigenRumor that take into account factors like blog activity, links, and comments. MailRank and TrustRank are discussed for using reputation propagation and spam scores to detect spam. The challenges of building measurable and dynamic reputation systems that avoid an arms race are also summarized.

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Reputation Systems II Sybil Attack, BlogRank, B2Rank, EigenRumor, MailRank, TrustRunk Yury Lifshits Caltech http://yury.name Caltech CMI Seminar March 4, 2008 1 / 22
Outline 1 Sybil Attack 2 Ranking Blogs 3 Reputations For Fighting Spam 4 Conclusions 2 / 22
1 Sybil Attack 3 / 22
Sybil Attack Graph of trust-weighted edges n honest nodes + adversary overall trust value on attack edges (honest-malicious) is limited 4 / 22
Sybil Attack Graph of trust-weighted edges n honest nodes + adversary overall trust value on attack edges (honest-malicious) is limited Question: whether splitting adversarial node into many is beneficial for acquiring higher reputation (rank)? 4 / 22
Negative Result Assume reputation scores remain the same under isomorphism. Is it sybilproof? 5 / 22
Negative Result Assume reputation scores remain the same under isomorphism. Is it sybilproof? Unfortunately, no. Attack strategy? 5 / 22
Negative Result Assume reputation scores remain the same under isomorphism. Is it sybilproof? Unfortunately, no. Attack strategy? Answer: double the graph. 5 / 22
Positive Results (1/3) General form of trust flow reputations: r(x) = max trust(p) Ptx p∈Ptx Notation: t is pre-trusted node Pxy is a family of disjoint paths from t to x 6 / 22
Positive Results (2/3) Assumptions: 1 Extending path nonincreases the trust(p) 2 and trust are monotone to number of paths and edges values, respectively 3 Splitting a path into two does not increase value 7 / 22
Positive Results (2/3) Assumptions: 1 Extending path nonincreases the trust(p) 2 and trust are monotone to number of paths and edges values, respectively 3 Splitting a path into two does not increase value 4 = max 7 / 22
Positive Results (3/3) Under assumptions (1-3) sybil attack does not increase adversary’s reputation 8 / 22
Positive Results (3/3) Under assumptions (1-3) sybil attack does not increase adversary’s reputation Under assumptions (1-4) sybil attack does not increase adversary’s rank 8 / 22
Positive Results (3/3) Under assumptions (1-3) sybil attack does not increase adversary’s reputation Under assumptions (1-4) sybil attack does not increase adversary’s rank Proof? 8 / 22
SybilGuard (1/2) Assume number of attack edges is A = o( n/ log n) System is distributed, honest nodes follow the same protocol Can an honest node t identify (w.h.p.) 2A + 1 nodes in such a way that at most A of them are powered by adversary? 9 / 22
SybilGuard (2/2) For every node fix a bijective mapping from in-edges to out-edges Take a walk from t of length at most n log n using bijection routing At some point make a random switch, than continue another n log n steps using backwalk routing Report a point. Repeat, until 2A + 1 points are collected 10 / 22
SybilGuard (2/2) For every node fix a bijective mapping from in-edges to out-edges Take a walk from t of length at most n log n using bijection routing At some point make a random switch, than continue another n log n steps using backwalk routing Report a point. Repeat, until 2A + 1 points are collected Claim w.h.p. at most A reported nodes are malicious 10 / 22
2 Ranking Blogs 11 / 22
Ranking Blogs: Factors Entities: blogs, posts, communities, comments, brand names, external websites Frineds, blogroll, subscriptions, hyperlinks, visitors, clicks, votes Time Tags 12 / 22
BlogRank Any ideas how to rank blogs? 13 / 22
BlogRank Any ideas how to rank blogs? Why not just PageRank? 13 / 22
BlogRank Any ideas how to rank blogs? Why not just PageRank? Wait a minute, for which graph? 13 / 22
BlogRank Any ideas how to rank blogs? Why not just PageRank? Wait a minute, for which graph? Linked blogs: Hyperlinks, blogrolls Common commentors/authors, tags, co-references to news 13 / 22
B2Rank B2Rank(x) = BlogReputation × PostQuality 14 / 22
B2Rank B2Rank(x) = BlogReputation × PostQuality BlogReputation is computed in PageRank style for blogroll graph with one change: Blogroll links are weighted by activity level (frequency of blogging and commenting) 14 / 22
B2Rank B2Rank(x) = BlogReputation × PostQuality BlogReputation is computed in PageRank style for blogroll graph with one change: Blogroll links are weighted by activity level (frequency of blogging and commenting) PostQuality is average for PageRank-style score of blog posts Post-to-post links are weighted by referring post activity and time difference 14 / 22
EigenRumor (1/2) Picture from “The EigenRumor Algorithm for Ranking Blogs” paper 15 / 22
EigenRumor (2/2) Notation: ¯: reputation score for posts r ¯ ¯ a, h: authority and hub scores for bloggers P, E: provision and evaluation matrices 16 / 22
EigenRumor (2/2) Notation: ¯: reputation score for posts r ¯ ¯ a, h: authority and hub scores for bloggers P, E: provision and evaluation matrices ¯ ¯ = αPT a + (1 − α)ET h r ¯ ¯ a = P¯, h = E¯ ¯ r r 16 / 22
EigenRumor (2/2) Notation: ¯: reputation score for posts r ¯ ¯ a, h: authority and hub scores for bloggers P, E: provision and evaluation matrices ¯ ¯ = αPT a + (1 − α)ET h r ¯ ¯ a = P¯, h = E¯ ¯ r r Solution: iterative algorithm for ¯: r T T ¯ = (αP P + (1 − α)E E)¯ r r 16 / 22
3 Reputations For Fighting Spam 17 / 22
Combining Two Scores Hyperlink graph 18 / 22
Combining Two Scores Hyperlink graph Pre-trusted nodes 18 / 22
Combining Two Scores Hyperlink graph Pre-trusted nodes Spam nodes 18 / 22
Combining Two Scores Hyperlink graph Pre-trusted nodes Spam nodes Reputation propagates in a forward manner 18 / 22
Combining Two Scores Hyperlink graph Pre-trusted nodes Spam nodes Reputation propagates in a forward manner Spam score propagates backwards 18 / 22
Combining Two Scores Hyperlink graph Pre-trusted nodes Spam nodes Reputation propagates in a forward manner Spam score propagates backwards Compute spam scores a-la PageRank 18 / 22
Combining Two Scores Hyperlink graph Pre-trusted nodes Spam nodes Reputation propagates in a forward manner Spam score propagates backwards Compute spam scores a-la PageRank Reweight hyperlink graph and pre-trusted nodes 18 / 22
Combining Two Scores Hyperlink graph Pre-trusted nodes Spam nodes Reputation propagates in a forward manner Spam score propagates backwards Compute spam scores a-la PageRank Reweight hyperlink graph and pre-trusted nodes Compute reputations a-la PageRank 18 / 22
4 Conclusions 19 / 22
Challenges Measurable objectives? Model for input data? Dynamic aspects of reputations? Digg-style ranking? Price of attack? Ranking in social networks? Ranking in RDF data? Billion dollar question: how to avoid arms race? 20 / 22
References K. Fujimura, T. Inoue, M. Sugisaki The EigenRumor Algorithm for Ranking Blogs A. Kritikopoulos, M. Sideri, I. Varlamis BlogRank: ranking weblogs based on connectivity and similarity features M.A. Tayebi, S.M. Hashemi, A. Mohades B2Rank: An Algorithm for Ranking Blogs Based on Behavioral Features A. Cheng, E. Friedman Sybilproof reputation mechanisms H. Yu, M. Kaminsky, P.B. Gibbons, A, Flaxman SybilGuard: defending against sybil attacks via social networks P.A. Chirita, J. Diederich, W. Nejdl MailRank: using ranking for spam detection Z. Gyongyi, H. Garcia-Molina, J. Pedersen Combating web spam with TrustRank M. Dalal Spam and popularity ratings for combating link spam 21 / 22
http://yury.name http://yury.name/reputation.html Ongoing project: http://businessconsumer.net 22 / 22
http://yury.name http://yury.name/reputation.html Ongoing project: http://businessconsumer.net Thanks for your attention! Questions? 22 / 22

More Related Content

Reputation Systems II

  • 1. Reputation Systems II Sybil Attack, BlogRank, B2Rank, EigenRumor, MailRank, TrustRunk Yury Lifshits Caltech http://yury.name Caltech CMI Seminar March 4, 2008 1 / 22
  • 2. Outline 1 Sybil Attack 2 Ranking Blogs 3 Reputations For Fighting Spam 4 Conclusions 2 / 22
  • 4. Sybil Attack Graph of trust-weighted edges n honest nodes + adversary overall trust value on attack edges (honest-malicious) is limited 4 / 22
  • 5. Sybil Attack Graph of trust-weighted edges n honest nodes + adversary overall trust value on attack edges (honest-malicious) is limited Question: whether splitting adversarial node into many is beneficial for acquiring higher reputation (rank)? 4 / 22
  • 6. Negative Result Assume reputation scores remain the same under isomorphism. Is it sybilproof? 5 / 22
  • 7. Negative Result Assume reputation scores remain the same under isomorphism. Is it sybilproof? Unfortunately, no. Attack strategy? 5 / 22
  • 8. Negative Result Assume reputation scores remain the same under isomorphism. Is it sybilproof? Unfortunately, no. Attack strategy? Answer: double the graph. 5 / 22
  • 9. Positive Results (1/3) General form of trust flow reputations: r(x) = max trust(p) Ptx p∈Ptx Notation: t is pre-trusted node Pxy is a family of disjoint paths from t to x 6 / 22
  • 10. Positive Results (2/3) Assumptions: 1 Extending path nonincreases the trust(p) 2 and trust are monotone to number of paths and edges values, respectively 3 Splitting a path into two does not increase value 7 / 22
  • 11. Positive Results (2/3) Assumptions: 1 Extending path nonincreases the trust(p) 2 and trust are monotone to number of paths and edges values, respectively 3 Splitting a path into two does not increase value 4 = max 7 / 22
  • 12. Positive Results (3/3) Under assumptions (1-3) sybil attack does not increase adversary’s reputation 8 / 22
  • 13. Positive Results (3/3) Under assumptions (1-3) sybil attack does not increase adversary’s reputation Under assumptions (1-4) sybil attack does not increase adversary’s rank 8 / 22
  • 14. Positive Results (3/3) Under assumptions (1-3) sybil attack does not increase adversary’s reputation Under assumptions (1-4) sybil attack does not increase adversary’s rank Proof? 8 / 22
  • 15. SybilGuard (1/2) Assume number of attack edges is A = o( n/ log n) System is distributed, honest nodes follow the same protocol Can an honest node t identify (w.h.p.) 2A + 1 nodes in such a way that at most A of them are powered by adversary? 9 / 22
  • 16. SybilGuard (2/2) For every node fix a bijective mapping from in-edges to out-edges Take a walk from t of length at most n log n using bijection routing At some point make a random switch, than continue another n log n steps using backwalk routing Report a point. Repeat, until 2A + 1 points are collected 10 / 22
  • 17. SybilGuard (2/2) For every node fix a bijective mapping from in-edges to out-edges Take a walk from t of length at most n log n using bijection routing At some point make a random switch, than continue another n log n steps using backwalk routing Report a point. Repeat, until 2A + 1 points are collected Claim w.h.p. at most A reported nodes are malicious 10 / 22
  • 18. 2 Ranking Blogs 11 / 22
  • 19. Ranking Blogs: Factors Entities: blogs, posts, communities, comments, brand names, external websites Frineds, blogroll, subscriptions, hyperlinks, visitors, clicks, votes Time Tags 12 / 22
  • 20. BlogRank Any ideas how to rank blogs? 13 / 22
  • 21. BlogRank Any ideas how to rank blogs? Why not just PageRank? 13 / 22
  • 22. BlogRank Any ideas how to rank blogs? Why not just PageRank? Wait a minute, for which graph? 13 / 22
  • 23. BlogRank Any ideas how to rank blogs? Why not just PageRank? Wait a minute, for which graph? Linked blogs: Hyperlinks, blogrolls Common commentors/authors, tags, co-references to news 13 / 22
  • 24. B2Rank B2Rank(x) = BlogReputation × PostQuality 14 / 22
  • 25. B2Rank B2Rank(x) = BlogReputation × PostQuality BlogReputation is computed in PageRank style for blogroll graph with one change: Blogroll links are weighted by activity level (frequency of blogging and commenting) 14 / 22
  • 26. B2Rank B2Rank(x) = BlogReputation × PostQuality BlogReputation is computed in PageRank style for blogroll graph with one change: Blogroll links are weighted by activity level (frequency of blogging and commenting) PostQuality is average for PageRank-style score of blog posts Post-to-post links are weighted by referring post activity and time difference 14 / 22
  • 27. EigenRumor (1/2) Picture from “The EigenRumor Algorithm for Ranking Blogs” paper 15 / 22
  • 28. EigenRumor (2/2) Notation: ¯: reputation score for posts r ¯ ¯ a, h: authority and hub scores for bloggers P, E: provision and evaluation matrices 16 / 22
  • 29. EigenRumor (2/2) Notation: ¯: reputation score for posts r ¯ ¯ a, h: authority and hub scores for bloggers P, E: provision and evaluation matrices ¯ ¯ = αPT a + (1 − α)ET h r ¯ ¯ a = P¯, h = E¯ ¯ r r 16 / 22
  • 30. EigenRumor (2/2) Notation: ¯: reputation score for posts r ¯ ¯ a, h: authority and hub scores for bloggers P, E: provision and evaluation matrices ¯ ¯ = αPT a + (1 − α)ET h r ¯ ¯ a = P¯, h = E¯ ¯ r r Solution: iterative algorithm for ¯: r T T ¯ = (αP P + (1 − α)E E)¯ r r 16 / 22
  • 32. Combining Two Scores Hyperlink graph 18 / 22
  • 33. Combining Two Scores Hyperlink graph Pre-trusted nodes 18 / 22
  • 34. Combining Two Scores Hyperlink graph Pre-trusted nodes Spam nodes 18 / 22
  • 35. Combining Two Scores Hyperlink graph Pre-trusted nodes Spam nodes Reputation propagates in a forward manner 18 / 22
  • 36. Combining Two Scores Hyperlink graph Pre-trusted nodes Spam nodes Reputation propagates in a forward manner Spam score propagates backwards 18 / 22
  • 37. Combining Two Scores Hyperlink graph Pre-trusted nodes Spam nodes Reputation propagates in a forward manner Spam score propagates backwards Compute spam scores a-la PageRank 18 / 22
  • 38. Combining Two Scores Hyperlink graph Pre-trusted nodes Spam nodes Reputation propagates in a forward manner Spam score propagates backwards Compute spam scores a-la PageRank Reweight hyperlink graph and pre-trusted nodes 18 / 22
  • 39. Combining Two Scores Hyperlink graph Pre-trusted nodes Spam nodes Reputation propagates in a forward manner Spam score propagates backwards Compute spam scores a-la PageRank Reweight hyperlink graph and pre-trusted nodes Compute reputations a-la PageRank 18 / 22
  • 40. 4 Conclusions 19 / 22
  • 41. Challenges Measurable objectives? Model for input data? Dynamic aspects of reputations? Digg-style ranking? Price of attack? Ranking in social networks? Ranking in RDF data? Billion dollar question: how to avoid arms race? 20 / 22
  • 42. References K. Fujimura, T. Inoue, M. Sugisaki The EigenRumor Algorithm for Ranking Blogs A. Kritikopoulos, M. Sideri, I. Varlamis BlogRank: ranking weblogs based on connectivity and similarity features M.A. Tayebi, S.M. Hashemi, A. Mohades B2Rank: An Algorithm for Ranking Blogs Based on Behavioral Features A. Cheng, E. Friedman Sybilproof reputation mechanisms H. Yu, M. Kaminsky, P.B. Gibbons, A, Flaxman SybilGuard: defending against sybil attacks via social networks P.A. Chirita, J. Diederich, W. Nejdl MailRank: using ranking for spam detection Z. Gyongyi, H. Garcia-Molina, J. Pedersen Combating web spam with TrustRank M. Dalal Spam and popularity ratings for combating link spam 21 / 22