Different methods and Conclusions Liqin Zhang
Different methods <ul><li>Basic models </li></ul><ul><li>Reputation models in peer-to-peer networks </li></ul><ul><li>Repu...
Rating systems <ul><li>Reputation is taken to be a function of the cumulative positive or negative  rating  for a seller o...
Basic models: <ul><li>Computational model </li></ul><ul><ul><li>Based on how much deeds exchanged  </li></ul></ul><ul><li>...
Computational model[2]:   <ul><li>If Reputation increase, trust increase </li></ul><ul><li>If trust increase, reciprocity ...
A Collaborative reputation mechanism:   <ul><li>Collaborative filtering </li></ul><ul><ul><li>To detect patterns among opi...
Reputation model in peer-to-peer[11]  <ul><li>P2P network:  </li></ul><ul><ul><li>peers cooperate to perform a critical fu...
Models in peer-to-peer networks <ul><li>Based on recommendation from other peers </li></ul><ul><ul><li>Combine with Bayesi...
Method 1: Reputation based on recommendation [11]
<ul><li>Recomendation from different kind of peers  </li></ul><ul><ul><li>Different weight </li></ul></ul><ul><ul><li>Upda...
Method2: based on global trust value ---Eigen Trust Algorithm[12] <ul><li>Decreases the number of downloads of unauthentic...
Reputation – Peer to Peer N/w <ul><li>Limited Reputation Sharing in P2P Systems[14] </li></ul><ul><ul><li>Techniques based...
Reputation models in Social networks[3~10] <ul><li>Social network:  </li></ul><ul><ul><li>a representation of the relation...
Importance of the nodes <ul><li>Proposal 1: all nodes are equal important </li></ul><ul><li>Proposal 2: some nodes are imp...
Models in social network <ul><li>Reputation extracting model: </li></ul><ul><ul><li>Ranking the reputation for each node i...
Reputation models in Social networks <ul><li>Extracting Reputation in Multi agent systems[8] </li></ul><ul><ul><li>Feedbac...
Reputation models in Social Networks: <ul><li>Social ReGreT[5]: </li></ul><ul><ul><li>Analysis social relation </li></ul><...
Conclusions <ul><li>Reputation is very important in electronic communities </li></ul><ul><li>Reputation can have different...
Conclusions <ul><li>Reputation can be classified to individual and group reputation, individual reputation can be further ...
Conclusions: Comparison methods <ul><li>Basic models: </li></ul><ul><ul><li>Computation model  </li></ul></ul><ul><ul><ul>...
Conclusions: Comparison methods <ul><li>In  P2P network,  </li></ul><ul><ul><li>Bayesian network model: </li></ul></ul><ul...
Conclusions: Comparison methods <ul><li>In social network: </li></ul><ul><ul><li>Can consider the  position  of an agent, ...
Conclusions: <ul><li>The reputation computation need consider recommendation of “friends”, the position of the referrer, w...
References <ul><li>[1]. Computational Models of Trust and Reputation: Agents, Evolutionary Games, and Social Networks, www...
References <ul><li>[7]. Finding others online: reputation systems for social online spaces, Proceedings of the SIGCHI conf...
References <ul><li>[12]  S. D. Kamvar, M. T. Schlosser, and H. Garcia-Molina. The Eigen Trust algorithm for reputation man...
References <ul><li>[17] G.L. Rein, Reputation Information Systems: A Reference Model, Proceedings of the 38th Hawaii Inter...
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Different methods and Conclusions Liqin Zhang

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Different methods and Conclusions Liqin Zhang

  1. 1. Different methods and Conclusions Liqin Zhang
  2. 2. Different methods <ul><li>Basic models </li></ul><ul><li>Reputation models in peer-to-peer networks </li></ul><ul><li>Reputation models in social networks </li></ul>
  3. 3. Rating systems <ul><li>Reputation is taken to be a function of the cumulative positive or negative rating for a seller or buyer </li></ul><ul><li>Rating model </li></ul><ul><ul><li>Uniform context environment: heard rating from one agent </li></ul></ul><ul><ul><li>Multiple context environment: from multiple agents </li></ul></ul><ul><li>Centrality-based rating: based on in/out degree of a node </li></ul><ul><li>Preference-based rating: Consider the preferences of each member when selecting the reputable members </li></ul><ul><li>Bayesian estimate rating: to compute reputation with recommendation of different context </li></ul>
  4. 4. Basic models: <ul><li>Computational model </li></ul><ul><ul><li>Based on how much deeds exchanged </li></ul></ul><ul><li>Collaborative model </li></ul><ul><ul><li>Based on recommendation from similar tasted people </li></ul></ul>
  5. 5. Computational model[2]: <ul><li>If Reputation increase, trust increase </li></ul><ul><li>If trust increase, reciprocity increase </li></ul><ul><li>If reciprocity increase, reputation increase </li></ul>Reputation Net benefit Reciprocity Trust Reciprocity: mutual exchange of deeds
  6. 6. A Collaborative reputation mechanism: <ul><li>Collaborative filtering </li></ul><ul><ul><li>To detect patterns among opinions of different users </li></ul></ul><ul><ul><li>Make recommendation based on rating of people with similar taste </li></ul></ul><ul><li>Fake rating: </li></ul><ul><ul><li>1. Rate more than once </li></ul></ul><ul><ul><li>2. Fake identity </li></ul></ul><ul><ul><li>Solve: rating from people with high reputation in network weighted more </li></ul></ul>
  7. 7. Reputation model in peer-to-peer[11] <ul><li>P2P network: </li></ul><ul><ul><li>peers cooperate to perform a critical function in a decentralized manner </li></ul></ul><ul><ul><li>Peers are both consumers and providers of resources </li></ul></ul><ul><ul><li>Peers can access each other directly </li></ul></ul><ul><li>Allow peers to represent and update their trust in other peers in open networks for sharing files </li></ul>
  8. 8. Models in peer-to-peer networks <ul><li>Based on recommendation from other peers </li></ul><ul><ul><li>Combine with Bayesian network </li></ul></ul><ul><li>Based on global trust value </li></ul>
  9. 9. Method 1: Reputation based on recommendation [11]
  10. 10. <ul><li>Recomendation from different kind of peers </li></ul><ul><ul><li>Different weight </li></ul></ul><ul><ul><li>Update reference’s weight </li></ul></ul><ul><li>Final reputation and trust is computed based on Bayesian network </li></ul><ul><li>Solve: reputation on different aspects of a peer </li></ul>
  11. 11. Method2: based on global trust value ---Eigen Trust Algorithm[12] <ul><li>Decreases the number of downloads of unauthenticated files in a peer-to-peer file sharing network by assigning a unique global trust value </li></ul><ul><li>A distributed and secure method to compute global trust values based on power iteration </li></ul><ul><li>Peers use these global trust values to choose the peers from whom they download and share files </li></ul>
  12. 12. Reputation – Peer to Peer N/w <ul><li>Limited Reputation Sharing in P2P Systems[14] </li></ul><ul><ul><li>Techniques based on collecting reputation information which uses only limited or no information sharing between nodes. </li></ul></ul><ul><ul><li>Effect of limited reputation information sharing in a peer-to-peer system. </li></ul></ul><ul><ul><ul><li>Efficiency </li></ul></ul></ul><ul><ul><ul><li>Load distribution and balancing </li></ul></ul></ul><ul><ul><ul><li>Message traffic </li></ul></ul></ul>
  13. 13. Reputation models in Social networks[3~10] <ul><li>Social network: </li></ul><ul><ul><li>a representation of the relationships existing within a community </li></ul></ul><ul><li>Each node provide both services and referrals for services to each other </li></ul>
  14. 14. Importance of the nodes <ul><li>Proposal 1: all nodes are equal important </li></ul><ul><li>Proposal 2: some nodes are important than others </li></ul><ul><ul><li>Referrals from A, B, C,D,E is more important than those nodes in only local network – pivot </li></ul></ul><ul><ul><li>You may trust the referral from a friend of you than strangers </li></ul></ul><ul><ul><li>You may also need consider the your preference regarding to referral </li></ul></ul>
  15. 15. Models in social network <ul><li>Reputation extracting model: </li></ul><ul><ul><li>Ranking the reputation for each node in network based on their location </li></ul></ul><ul><li>Social ReGreT model: </li></ul><ul><ul><li>Based on information collected from three dimension </li></ul></ul>
  16. 16. Reputation models in Social networks <ul><li>Extracting Reputation in Multi agent systems[8] </li></ul><ul><ul><li>Feedback after interaction between agents </li></ul></ul><ul><ul><li>Also consider the position of an agent in social network </li></ul></ul><ul><ul><ul><li>Node ranking: creating a ranking of reputation ratings of community members </li></ul></ul></ul><ul><ul><ul><ul><li>Based on the in-degree and out-degree of a node (like Pagerank) </li></ul></ul></ul></ul>
  17. 17. Reputation models in Social Networks: <ul><li>Social ReGreT[5]: </li></ul><ul><ul><li>Analysis social relation </li></ul></ul><ul><ul><li>To identify valuable features in e-commerce </li></ul></ul><ul><ul><li>Aimed to solve the problem of referrer’s false, biased or incomplete information </li></ul></ul><ul><ul><li>Based on three dimensions of reputation </li></ul></ul><ul><ul><ul><li>If use only interaction inf. --- individual dimension(single) </li></ul></ul></ul><ul><ul><ul><li>If also use inf. from others --- social dimension (multiple) </li></ul></ul></ul><ul><ul><ul><li>Three dimension: </li></ul></ul></ul><ul><ul><ul><ul><li>Witness reputation: from pivot agents </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Neighborhood reputation: </li></ul></ul></ul></ul><ul><ul><ul><ul><li>System reputation: default reputation value based on the role played by the target agent </li></ul></ul></ul></ul>
  18. 18. Conclusions <ul><li>Reputation is very important in electronic communities </li></ul><ul><li>Reputation can have different notation such as “general estimate a person”, “perception that an agent has of another’s intentions and norms”… </li></ul><ul><li>Reputation systems can be grouped according to the nature of information they give about the object of interest and how the rating is generated, 4 reputation systems are discussed </li></ul>
  19. 19. Conclusions <ul><li>Reputation can be classified to individual and group reputation, individual reputation can be further classified </li></ul><ul><li>The challenge for reputation includes less feedback, negative feedback, un-honesty feedback (change name), context and location awareness </li></ul><ul><li>An agent can be honesty, malicious, evil, selfish </li></ul><ul><li>Discussed 7 metrics with benchmarks </li></ul>
  20. 20. Conclusions: Comparison methods <ul><li>Basic models: </li></ul><ul><ul><li>Computation model </li></ul></ul><ul><ul><ul><li>based on how much deeds exchanged </li></ul></ul></ul><ul><ul><ul><li>Can be used in P2P and Social network </li></ul></ul></ul><ul><ul><ul><li>Doesn’t consider references/recommendation, weight of deeds </li></ul></ul></ul><ul><ul><li>Collaborative model </li></ul></ul><ul><ul><ul><li>Based on the recommendation from similar tasted people </li></ul></ul></ul><ul><ul><ul><li>Recommendation is weighted based on referrer’s reputation – avoid fake recommendation </li></ul></ul></ul><ul><ul><ul><li>Doesn’t consider the location of referrer </li></ul></ul></ul>
  21. 21. Conclusions: Comparison methods <ul><li>In P2P network, </li></ul><ul><ul><li>Bayesian network model: </li></ul></ul><ul><ul><ul><li>Based on information collected from “friends” </li></ul></ul></ul><ul><ul><ul><li>Peers share recommendations </li></ul></ul></ul><ul><ul><ul><li>It allows to develop different trust regarding to different aspects of the peers’ capability </li></ul></ul></ul><ul><ul><ul><li>Overall trust need combine all aspect </li></ul></ul></ul><ul><ul><ul><li>Doesn’t consider location </li></ul></ul></ul>
  22. 22. Conclusions: Comparison methods <ul><li>In social network: </li></ul><ul><ul><li>Can consider the position of an agent, Pivot agents are more important than other agents </li></ul></ul><ul><ul><li>NodeRanking: </li></ul></ul><ul><ul><ul><li>Ranking the reputation in social network based on position </li></ul></ul></ul><ul><ul><ul><li>Used to find the pivot </li></ul></ul></ul><ul><ul><li>Social ReGreT model: </li></ul></ul><ul><ul><ul><li>Consider three dimension: </li></ul></ul></ul><ul><ul><ul><ul><li>Witness –pivot node </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Neighborhood recommendation </li></ul></ul></ul></ul><ul><ul><ul><ul><li>System value </li></ul></ul></ul></ul>
  23. 23. Conclusions: <ul><li>The reputation computation need consider recommendation of “friends”, the position of the referrer, weight for referrer </li></ul><ul><li>“ friends” may refer to its neighborhood, or the group of people who has the similar taste, or people you trust </li></ul><ul><li>Weight for referrer can avoid fake recommendation </li></ul><ul><li>No models consider all of the factors </li></ul>
  24. 24. References <ul><li>[1]. Computational Models of Trust and Reputation: Agents, Evolutionary Games, and Social Networks, www.cdm.csail.mit.edu/ftp/lmui/ computational%20models%20of%20trust%20and%20reputation.pdf </li></ul><ul><li>[2]. A computation model of Trust and Reputation, http://csdl2.computer.org/comp/proceedings/hicss/2002/1435/07/14350188.pdf </li></ul><ul><li>[3]. Trust and Reputation Management in a Small-World Network, ICMAS Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000), 2000 </li></ul><ul><li>[4]. How Social Structure Improves Distributed Reputation Systems, http://www.ipd.uka.de/~nimis/publications/ap2pc04.pdf </li></ul><ul><li>[5]. Social ReGreT, a reputation model based on social relations , ACM SIGecom Exchanges Volume 3 ,  Issue 1   Winter, 2002,Pages: 44 – 56 </li></ul><ul><li>[6]. Detecting deception in reputation management, Proceedings of the second international joint conference on Autonomous agents and multiagent systems , 2003 </li></ul>
  25. 25. References <ul><li>[7]. Finding others online: reputation systems for social online spaces, Proceedings of the SIGCHI conference on Human factors in computing systems: Changing our world, changing ourselves, 2002, Pages: 447 - 454   </li></ul><ul><li>[8]. J. Pujol and R. Sanguesa and J. Delgado, Extracting reputation in multi-agent systems by means of social network topology, In Proceedings of First International Joint pages 467--474, 2002 </li></ul><ul><li>[9]. J. Sabater and C. Sierra,Reputation and social network analysis in multi-agent systems, Proceedings of the first international joint conference on Autonomous agents and multiagent systems: P 475 – 482,2002 </li></ul><ul><li>[10]. Trust evaluation through relationship analysis, Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems,P1005 – 1011, 2005 </li></ul><ul><li>[11] Trust and Reputation model in peer-to-peer networks, www.cs.usask.ca/grads/ yaw181/publications/120_wang_y.pdf </li></ul>
  26. 26. References <ul><li>[12] S. D. Kamvar, M. T. Schlosser, and H. Garcia-Molina. The Eigen Trust algorithm for reputation management in p2p networks. In Proceedings of the Twelfth International World Wide Web Conference, 2003. </li></ul><ul><li>[13] Lars Rasmusson and Sverker Jansson, “Simulated social control. for secure internet commerce,” in New Security Paradigms ’96. September 1996 </li></ul><ul><li>[14] S. Marti, H. Garcial-Molina, Limited Reputation Sharing in P2P Systems, ACM Conference on Electronic Commerce (EC'04) </li></ul><ul><li>[15] Lik Mui, Computational Models of Trust and Reputation: Agents, Evolutionary Games, and Social Networks, Ph. D Dissertation, Massachusetts Institute of Technology </li></ul><ul><li>[16] Goecks, J. and Mynatt E.D. (2002). Enabling privacy management in ubiquitous computing environments through trust and reputation systems. Workshop on Privacy in Digital Environments: Empowering Users. Proceedings of CSCW 2002 </li></ul>
  27. 27. References <ul><li>[17] G.L. Rein, Reputation Information Systems: A Reference Model, Proceedings of the 38th Hawaii International Conference on System Sciences - 2005 </li></ul>
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