Different methods and Conclusions Liqin Zhang

Different methods Basic models Reputation models in peer-to-peer networks Reputation models in social networks

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

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

Computational model[2]: If Reputation increase, trust increase If trust increase, reciprocity increase If reciprocity increase, reputation increase Reputation Net benefitReciprocityTrust Reciprocity: mutual exchange of deeds

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

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

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

Method 1: Reputation based on recommendation [11]

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

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

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

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

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

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

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)

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

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

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

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

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

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

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

References [1]. Computational Models of Trust and Reputation: Agents, Evolutionary Games, and Social Networks, computational%20models%20of%20trust%20and%20reputation.pdf [2]. A computation model of Trust and Reputation, [3]. Trust and Reputation Management in a Small-World Network, ICMAS Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000), 2000 [4]. How Social Structure Improves Distributed Reputation Systems, [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

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: [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 , 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, yaw181/publications/120_wang_y.pdf

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, [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

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