Different methods and Conclusions

1. Different methods and Conclusions Liqin Zhang

2. Different methods • Basic models • Reputation models in peer-to-peer networks • Reputation models in social networks

3. Rating systems • Reputation is taken to be a function of the cumulative positive or negative rating for a seller or buyer • Rating model • Uniform context environment: heard rating from one agent • Multiple context environment: from multiple agents • Centrality-based rating: based on in/out degree of a node • Preference-based rating: Consider the preferences of each member when selecting the reputable members • Bayesian estimate rating: to compute reputation with recommendation of different context

4. Basic models: • Computational model • Based on how much deeds exchanged • Collaborative model • Based on recommendation from similar tasted people

5. Computational model[2]: • If Reputation increase, trust increase • If trust increase, reciprocity increase • If reciprocity increase, reputation increase Reputation Reciprocity: mutual exchange of deeds Trust Reciprocity Net benefit

6. A Collaborative reputation mechanism: • Collaborative filtering • To detect patterns among opinions of different users • Make recommendation based on rating of people with similar taste • Fake rating: • 1. Rate more than once • 2. Fake identity • Solve: rating from people with high reputation in network weighted more

7. Reputation model in peer-to-peer[11] • P2P network: • peers cooperate to perform a critical function in a decentralized manner • Peers are both consumers and providers of resources • Peers can access each other directly • Allow peers to represent and update their trust in other peers in open networks for sharing files

8. Models in peer-to-peer networks • Based on recommendation from other peers • Combine with Bayesian network • Based on global trust value

9. Method 1: Reputation based on recommendation [11]

10. Recomendation from different kind of peers • Different weight • Update reference’s weight • Final reputation and trust is computed based on Bayesian network • Solve: reputation on different aspects of a peer

11. Method2: based on global trust value---Eigen Trust Algorithm[12] • Decreases the number of downloads of unauthenticated files in a peer-to-peer file sharing network by assigning a unique global trust value • A distributed and secure method to compute global trust values based on power iteration • Peers use these global trust values to choose the peers from whom they download and share files

12. Reputation – Peer to Peer N/w • Limited Reputation Sharing in P2P Systems[14] • Techniques based on collecting reputation information which uses only limited or no information sharing between nodes. • Effect of limited reputation information sharing in a peer-to-peer system. • Efficiency • Load distribution and balancing • Message traffic

13. Reputation models in Social networks[3~10] • Social network: • a representation of the relationships existing within a community • Each node provide both services and referrals for services to each other

14. Importance of the nodes • Proposal 1: all nodes are equal important • Proposal 2: some nodes are important than others • Referrals from A, B, C,D,E is more important than those nodes in only local network – pivot • You may trust the referral from a friend of you than strangers • You may also need consider the your preference regarding to referral

15. Models in social network • Reputation extracting model: • Ranking the reputation for each node in network based on their location • Social ReGreT model: • Based on information collected from three dimension

16. Reputation models in Social networks • Extracting Reputation in Multi agent systems[8] • Feedback after interaction between agents • Also consider the position of an agent in social network • Node ranking: creating a ranking of reputation ratings of community members • Based on the in-degree and out-degree of a node (like Pagerank)

17. Reputation models in Social Networks: • Social ReGreT[5]: • Analysis social relation • To identify valuable features in e-commerce • Aimed to solve the problem of referrer’s false, biased or incomplete information • Based on three dimensions of reputation • If use only interaction inf. --- individual dimension(single) • If also use inf. from others --- social dimension (multiple) • Three dimension: • Witness reputation: from pivot agents • Neighborhood reputation: • System reputation: default reputation value based on the role played by the target agent

18. Conclusions • Reputation is very important in electronic communities • Reputation can have different notation such as “general estimate a person”, “perception that an agent has of another’s intentions and norms”… • 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

19. Conclusions • Reputation can be classified to individual and group reputation, individual reputation can be further classified • The challenge for reputation includes less feedback, negative feedback, un-honesty feedback (change name), context and location awareness • An agent can be honesty, malicious, evil, selfish • Discussed 7 metrics with benchmarks

20. Conclusions: Comparison methods • Basic models: • Computation model • based on how much deeds exchanged • Can be used in P2P and Social network • Doesn’t consider references/recommendation, weight of deeds • Collaborative model • Based on the recommendation from similar tasted people • Recommendation is weighted based on referrer’s reputation – avoid fake recommendation • Doesn’t consider the location of referrer

21. Conclusions: Comparison methods • In P2P network, • Bayesian network model: • Based on information collected from “friends” • Peers share recommendations • It allows to develop different trust regarding to different aspects of the peers’ capability • Overall trust need combine all aspect • Doesn’t consider location

22. Conclusions: Comparison methods • In social network: • Can consider the position of an agent, Pivot agents are more important than other agents • NodeRanking: • Ranking the reputation in social network based on position • Used to find the pivot • Social ReGreT model: • Consider three dimension: • Witness –pivot node • Neighborhood recommendation • System value

23. Conclusions: • The reputation computation need consider recommendation of “friends”, the position of the referrer, weight for referrer • “friends” may refer to its neighborhood, or the group of people who has the similar taste, or people you trust • Weight for referrer can avoid fake recommendation • No models consider all of the factors

24. References [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 [2]. A computation model of Trust and Reputation, http://csdl2.computer.org/comp/proceedings/hicss/2002/1435/07/14350188.pdf [3].Trust and Reputation Management in a Small-World Network,ICMASProceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000), 2000 [4]. How Social Structure Improves Distributed Reputation Systems, http://www.ipd.uka.de/~nimis/publications/ap2pc04.pdf [5]. Social ReGreT, a reputation model based on social relations , ACM SIGecom Exchanges Volume 3 ,  Issue 1   Winter, 2002,Pages: 44 – 56 [6]. Detecting deception in reputation management, Proceedings of the second international joint conference on Autonomous agents and multiagent systems , 2003

25. References [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   [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 [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: P475 – 482,2002 [10]. Trust evaluation through relationship analysis, Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems,P1005 – 1011, 2005 [11] Trust and Reputation model in peer-to-peer networks, www.cs.usask.ca/grads/ yaw181/publications/120_wang_y.pdf

26. References [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. [13] Lars Rasmusson and Sverker Jansson, “Simulated social control. for secure internet commerce,” in New Security Paradigms ’96. September 1996 [14] S. Marti, H. Garcial-Molina, Limited Reputation Sharing in P2P Systems, ACM Conference on Electronic Commerce (EC'04) [15] Lik Mui, Computational Models of Trust and Reputation: Agents, Evolutionary Games, and Social Networks, Ph. D Dissertation, Massachusetts Institute of Technology [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

27. References [17] G.L. Rein, Reputation Information Systems: A Reference Model, Proceedings of the 38th Hawaii International Conference on System Sciences - 2005