escuela técnica superior                                       de ingeniería informáticaPolarityTrust: measuring Trust and...
Motivation♦ Example: on-line marketplaces
Motivation
Motivation                    How can I make the most from these                    transactions?                    Selli...
Motivation             ♦ Δ Reputation => Δ Sales             ♦ Gaining high reputation:              ●                  Ob...
Motivation♦ Goals:  ●      Compute a ranking of users according to their      trustworthiness  ●      Process a network wi...
Roadmap♦ Introduction♦ PolarityTrust♦ Evaluation♦ Conclusions
Introduction♦ Trust and Reputation Systems (TRS) manage  trustworthiness of users in social networks.♦ Common mechanisms: ...
Introduction♦ Users feedback needed!♦ Problems:  ●      Positive bias  ●      Incentives for users feedback  ●      Cold-s...
Introduction♦ Malicious users strategies to gain high reputation:            ♦ Orchestrated attacks: Obtaining positive   ...
Introduction♦ Malicious users strategies to gain high reputation:            ♦ Orchestrated attacks: Obtaining positive   ...
Introduction♦ Malicious users strategies to gain high reputation:            ♦ Camouflage behind good behavior: selling   ...
Introduction♦ Malicious users strategies to gain high reputation:            ♦ Malicious spies: using a honest account to ...
Introduction♦ Malicious users strategies to gain high reputation:            ♦ Camouflage behind judgments: giving        ...
PolarityTrust♦ Graph-based ranking algorithm♦ Two scores for each node: PT⁺ and PT⁻♦ Propagation of trust and distrust ove...
PolarityTrust♦ Propagation mechanism:  ●      Given a set of trustworthy users  ●      Their PT⁺ and PT⁻ scores are propag...
PolarityTrust♦ Propagation rules:  ●      Positive opinions => direct relation between scores  ●      Negative opinions =>...
Evaluation♦ Baselines:  ●      EigenTrust  ●      Fans Minus Freaks♦ Dataset:  ●      Randomly generated graphs: Barabasi ...
Evaluation♦ Performance against common attacks: Models   ET    FmF   PT    PT+NN        Models      ET     FmF   PT   PT+N...
Evaluation♦ Performance against incremental number of malicious users:
Conclusions♦ Something
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PolarityTrust: measuring Trust and Reputation in Social Networks

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PolarityTrust is a Trust and Reputation System that demotes malicious users in Social Networks.

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F. Javier Ortega; José A. Troyano; Fermín L. Cruz; Fernando Enríquez de Salamanca. “PolarityTrust: Measuring Trust and Reputation in Social Networks”. Fourth International Conference on Internet Technologies and Applications (ITA’11). Wrexham, North Wales, United Kingdom.

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  • Hi Javier

    I like your presentation, and I think the development of a trust network is needed. But my biggest concern, apart from the ones you've described, is the false positive issue of friends 'boosting' each other so as to mutually benefit. I think that there needs to be an algorithm developed which looks at the topics between two people in a social network, and the length/timing of said comments. It needs a lot of thought before the IT side of things is even considered....
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PolarityTrust: measuring Trust and Reputation in Social Networks

  1. 1. escuela técnica superior de ingeniería informáticaPolarityTrust: measuring Trust and Reputation in Social Networks F. Javier Ortega javierortega@us.es José A. Troyano troyano@us.es Fermín L. Cruz fcruz@us.es Fernando Enríquez de Salamanca fenros@us.es Departamento de Lenguajes y Sistemas Informáticos
  2. 2. Motivation♦ Example: on-line marketplaces
  3. 3. Motivation
  4. 4. Motivation How can I make the most from these transactions? Selling more products but cheaper? Selling rare (and maybe expensive) articles? Free shipping? How can I choose the best seller? The one with highest amount of sales? The one with most positive opinions? The cheapest one?
  5. 5. Motivation ♦ Δ Reputation => Δ Sales ♦ Gaining high reputation: ● Obtain (false) positive opinions from other accounts (not neccesarily other users). ● Sell some bargains to obtain high reputation from the buyers. ● Give negative opinions for sellers that can be competitors.
  6. 6. Motivation♦ Goals: ● Compute a ranking of users according to their trustworthiness ● Process a network with positive and negative links (opinions) between the nodes (users) ● Avoid the effects of the actions performed by malicious users in order to increase their reputation
  7. 7. Roadmap♦ Introduction♦ PolarityTrust♦ Evaluation♦ Conclusions
  8. 8. Introduction♦ Trust and Reputation Systems (TRS) manage trustworthiness of users in social networks.♦ Common mechanisms: ● Moderators (on-line forums) ● Votes from users to users (eBay) ● Karma (Slashdot, Meneame) ● Graph-based ranking algorithms (EigenTrust)
  9. 9. Introduction♦ Users feedback needed!♦ Problems: ● Positive bias ● Incentives for users feedback ● Cold-start problem ● Exit problem ● Duplicity of identities
  10. 10. Introduction♦ Malicious users strategies to gain high reputation: ♦ Orchestrated attacks: Obtaining positive opinions from other accounts (not neccesarily other users). ♦ Camouflage behind good behavior: selling some bargains to obtain high reputation from the buyers. ♦ Malicious spies: using a honest account to provide positive opinions to a malicious user. ♦ Camouflage behind judgments: giving negative opinions from seller that can be competitors.
  11. 11. Introduction♦ Malicious users strategies to gain high reputation: ♦ Orchestrated attacks: Obtaining positive opinions from other accounts (not neccesarily other users). 6 1 7 2 0 3 9 5 8 4
  12. 12. Introduction♦ Malicious users strategies to gain high reputation: ♦ Camouflage behind good behavior: selling some bargains to obtain high reputation from the buyers. 6 1 7 2 0 3 9 5 8 4
  13. 13. Introduction♦ Malicious users strategies to gain high reputation: ♦ Malicious spies: using a honest account to provide positive opinions to a malicious user. 6 1 7 2 0 3 9 8 4 5
  14. 14. Introduction♦ Malicious users strategies to gain high reputation: ♦ Camouflage behind judgments: giving negative opinions from seller that can be competitors. 6 1 7 2 0 3 9 5 8 4
  15. 15. PolarityTrust♦ Graph-based ranking algorithm♦ Two scores for each node: PT⁺ and PT⁻♦ Propagation of trust and distrust over the network♦ PT⁺ and PT⁻ influence each other depending on the polarity of the links between a node and its neighbours.
  16. 16. PolarityTrust♦ Propagation mechanism: ● Given a set of trustworthy users ● Their PT⁺ and PT⁻ scores are propagated to their neighbours, and so on. 6 1 7 2 0 3 9 5 8 4
  17. 17. PolarityTrust♦ Propagation rules: ● Positive opinions => direct relation between scores ● Negative opinions => cross relation between scores♦ Non-negative Propagation extension: ● Avoid the propagation of negative opinions from negative users b b a a c c
  18. 18. Evaluation♦ Baselines: ● EigenTrust ● Fans Minus Freaks♦ Dataset: ● Randomly generated graphs: Barabasi and Albert model. ● Malicious users added in order to perform common attacks♦ Evaluation metrics: ● Number of inversions: bad users in good positions ● Incremental number of bad nodes
  19. 19. Evaluation♦ Performance against common attacks: Models ET FmF PT PT+NN Models ET FmF PT PT+NN A 50 0 0 0 A 50 0 0 0 B 197 36 0 0 B 197 36 0 0 C 63 207 94 94 B+C 155 873 27 27 D 86 9 9 9 B+C+D 169 871 26 26 E 74 4 0 0 B+C+D+E 183 849 38 36 A: No attacks B: Orchestrated attacks C: Camouflage behind good behaviour D: Malicious Spies E: Camouflage behind judgments
  20. 20. Evaluation♦ Performance against incremental number of malicious users:
  21. 21. Conclusions♦ Something

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