1. Reputation Prakash Kolan Liqin Zhang Venkatesh Kancherla

2. Introduction Internet – No longer just a medium for non-commercial informal information exchange between scientists and universities[13] – It has become a public network also used to support commercial transactions – Unclear what will happen when this extremely open network is used in the new context of commerce – Likely that the introduction of money will be the motivation for criminal activities previously considered uninteresting.

3. Introduction Expansion of the Internet – People and services are called upon to interact with independent parties in application areas like e-commerce, knowledge sharing, game playing etc. – Anyone is free to add what components (hardware and software) as he/she wishes – No central authority keeps track of who is using it and how – An electronic market with a centralized verifying authority that checks and certifies (human and electronic) participants would be a very non-open solution

4. Introduction Parties are autonomous and potentially subject to different administrative and legal domains Important that – Decentralized and open mechanisms exist that allow participants in a market to know something about other participants – Each participant should be able to identify trustworthy parties or correspondents with whom they should interact and untrustworthy correspondents with whom they should avoid interaction without having to rely on some external central authority

5. Need for Reputation We need Reputation because… – Internet is as an open system more like “a big city” than “a small village”[13] – Possible to act in any possible way without anyone being able to stop it. – Large amount of fraud and con men doing businesses and lots of harmful content floating out there – In a big city you can’t know who you are dealing with if you meet for the first time

6. Need for Reputation We need Reputation because… – How is it considered possible to negotiate, cooperate and perform online communication if there is no way of formally knowing the intentions of the other participants

7. Defining Reputation According to Oxford dictionary, reputation is the “Common or general estimate of a person with respect to characters or other qualities“[5] Reputation refers to a perception that an agent has of another’s intentions and norms[15] An entity’s reputation is some notion or report of its propensity to fulfill the trust placed in it (during a particular situation); its reputation is created through feedback from individuals who have previously interacted with the entity[16].

8. Defining Reputation Reputation, a distributed knowledge phenomenon, lives in time. When people interact with one another over time, the history of their past interactions informs others about their abilities and depositions[17]. Reputation systems are complex social systems that continually collect, aggregate, and distribute feedback about a person, an organization, a scholarly work, or some other entity, based on the assessments of others from their interactions or experiences with the entity[17].

9. Reputation Systems Types of Reputation Systems - Dimensions of Classification – Amount of effort required of users to generate reputations. – Explicit action by the users, such as giving ratings and scores – Users’ behavior, such as return rates – Ease of understanding by the user – Ease of implementation for developers – Degree of personal relevance of ratings to users Personal relevance is the degree to which ratings take into consideration the users’ likes and dislikes or the extent to which recommendations are tailored to the individual user

10. Reputation Systems Reputation systems can be grouped according to the nature of information they give about the object of interest and how the rating is generated. – Ranking Systems – Rating Systems – Collaborative Filtering Systems – Peer Based Reputation systems Implicit Peer Based Reputation systems Explicit Peer Based Reputation systems

11. Reputation Systems Ranking Systems – Use quantifiable measures of users’ behavior (implicit information) to generate a rating. – Example ranking systems – High score lists, information about length of membership, frequency of visits, replies etc. – Easy to implement and interpret and are most suited for goal oriented activities – These reputation systems typically only provide information about what kind of pattern users follow, and reveal little or no personally relevant information.

12. Reputation Systems Rating Systems – Use explicit evaluations given by users. – These evaluations are used to generate a weighted average for each object of interest. – Ratings are global, meaning that all users looking at the same object of interest will see the same score. – Provide more personally relevant information than ranking systems, they treat the population as a single homogenous group.

13. Reputation Systems Collaborative Rating Systems – These systems weight explicit or implicit evaluations by how much the rater and the user have concurred on other items – More sophisticated than rating systems, capturing significant amounts of personally relevant information— users’ likes and dislikes. – Most expensive to build, populate, maintain, as well as the most complicated for users to understand

14. Reputation Systems Peer Based Reputation Systems – Based on peer recommendations like friends and family – Peer-based recommendations (or social network based reputation systems), whether they are given explicitly or inferred through the observations of peer behavior, are a significant influence on everyday decision-making – The social context provided by ‘friend of a friend’ recommendations should be especially important in socially- oriented situations – The more social the situation, the more important peer based information is.

15. Reputation Systems Implicit Peer Based Reputation Systems – These systems track the behavior of a user’s ‘friends,’ generating ratings from this data. – Such systems observe what a user’s friends do (e.g., with whom they interact, what they look at, what they buy), and make recommendations accordingly. – These types of systems is that they provide information that is very socially relevant and tailored to the individual. – Potential drawbacks are the implementation costs, privacy concerns, and that such ratings might be difficult to understand for users

16. Reputation Systems Explicit Peer Based Reputation Systems – These systems rely on the evaluations given by a user’s ‘friends – Users select a group of ‘friends’ or trusted raters, and the evaluations made by this group are used to generate composite ratings. – These system weights or filters ratings based on who we know and choose to trust. – Ratings are highly relevant and tailored to the user. – Drawbacks include implement costs and difficulty in understanding

17. Reputation Systems

18. Notions of Reputation Reputation Typology Reputation can be viewed as a global or personalized quantity[15]

19. Notions of Reputation Individual & Group reputation Reputation is a function of the cumulative ratings on users by others for a individual A firm’s (group) reputation can be modeled as the average of all its members’ individual reputation

20. Notions of Reputation Direct & Indirect( Individual) reputation Reputation estimates by an evaluator based on direct experiences (seen or experienced by the evaluating agent first hand) Reputation estimates that are based on second-hand evidence (such as by word- of-mouth).

21. Notions of Reputation Direct Reputation Reputation based on actual encounter with the reputed agent Reputation based on evaluator’s rating for the reputed agent

22. Notions of Reputation Indirect Reputation Reputation based on the prior belief regarding the reputed agent Reputation for the reputed agent based on the group he belongs to Reputation garnered from different evaluating agents for the reputed agent

23. Requirements

24. Challenges in Eliciting feedback The first is that people may not bother to provide feedback at all. For example, when a trade is completed successfully at eBay, there is little incentive to spend another few minutes filling out a form People could be paid for providing feedback Secondly,It is especially difficult to elicit negative feedback. For example, at eBay it is common practice to negotiate first before resorting to negative feedback. Therefore, only really bad performances are reported.

25. Challenges in Eliciting feedback The third difficulty is assuring honest reports. One party could blackmail another—that is, threaten to post negative feedback unrelated to actual performance. At the other extreme, in order to accumulate positive feedback a group of people might collaborate and rate each other positively, artificially inflating their reputations.

26. Challenges in Distributing feedback The first is name changes. At many sites, people choose a pseudonym when they register. If they register again, they can choose another pseudonym, effectively erasing prior feedback. Two methods to avoid Name Changes :  Game theoretic analysis  Another alternative is to prevent name changes, either by using real names, or by preventing people from acquiring multiple pseudonyms, a technique called once-in-a-lifetime pseudonyms

27. Challenges in Distributing feedback A second difficulty in distributing feedback stems from lack of portability between systems. Amazon.com initially allowed users to import their ratings from eBay. eBay protested vigorously, claiming that their user ratings were proprietary. Ultimately Amazon discontinued its rating- import service. Efforts are underway to construct a more universal framework. For example, virtualfeedback.com provides a rating service for users across different systems, but it has yet to gain wide public acceptance.

28. Challenges in Distributing feedback Finally,There is also a potential difficulty in aggregating and displaying feedback so that it is truly useful in influencing future decisions about who to trust. eBay displays the net feedback (positives minus negatives). Other sites such as Amazon display an average.

29. Context and location awareness Another important consideration is the context and location awareness, as many of the applications are sensitive to the context or the location of the transactions. For example, the functionality of the transaction is an important context to be incorporated into the trust metric. Amazon.com may be trustworthy on selling books but not on providing medical devices.

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

31. 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

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

33. 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

34. 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

35. 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

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

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

38. 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

39. 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

40. 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

41. 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

42. 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

43. 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

44. 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)

45. 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

46. Metrics The algorithm used to calculate an agent’s reputation is the metric of the reputation system. The strength of a metric is measured by its resistance against different threat models, i.e, different types of hostile agents.

47. Formal Model The model provides an abstract view of a reputation system that allows the comparison of the core metrics of different reputation systems. According to definition of reputation a transaction between two peers is the basis of a rating. An agent cannot rate another one without having had a transaction with him.

48. Formal Model A is the set of agents. C is the context of a transaction. set C = T ×V where T = {0, 1,..., tnow} is the set of times and V is the set of transaction values E is the set of all encounters between different agents that have happened until now.

49. Formal Model An encounter contains information about the participating peers and the context: A rating is a mapping between a target agent “a belongs to A” and an encounter “e belongs to E” to the set of all possible ratings Q: In the simple case Q is a small set of possible values: Q ebay = {−1, 0, 1}

50. Formal Model Ea represents the subset of all encounters in which a has participated and received a rating: All encounters between a and b with a valid rating for a are: subset of all most recent encounters between a and other agents.

51. Formal Model The reputation of an agent a belongs to A is defined by the function r : A × T ->R. In short r(a)=r(a,t now ) A complete Metric M is defined as M=(p,r,Q,R,r 0 )

52. METRICS IN REPUTATION SYSTEM Accumulative Systems Average Systems Blurred Systems OnlyLast Systems EigenTrust System

53. Accumulative Systems If a system accumulates all given ratings to get the overall reputation of an agent we call it an accumulative system. Example Ebay system Possible ratings are p : A × E –> {−1, 0, 1}. The basic idea of these metrics is, that the more often an agent behaves in a good way the more sure can the others be, that this agent is an honest one.

54. Accumulative Systems The reputation of an agent “a belongs to A” computes with (ebay) No transaction values and multiple ratings The reputation in value system is given by

55. This kind of reputation system computes the reputation for an agent as the average of all ratings the agent has received The idea of this metric is, that agents behave the same way most of their lifetime. Unusual ratings have only little weight in the computation of the final reputation The simulated systems use p : A × E -> {−1, 0, 1} Average Systems

56. Average system The reputation of an agent “a belongs to A” in the Average-system without considering multiple ratings and transaction values is: In average-value system

57. Blurred Systems These reputation systems compute a weighted sum of all ratings. The older a rating is, the less it influences the current reputation Possible ratings are p: A × E -> {−1, 0, 1}

58. Blurred System The reputation of an agent “a belongs to A” without considering transaction values is: With consideration of transaction values:

59. OnlyLast System This system considers the most recent rating of an agent Ratings are p: A × E -> {−1, 0, 1} Here we expect an agent to behave like he did last time, no matter what he did before.

60. OnlyLast System Without considering transaction values in the OnlyLast system the reputation of an agent “a belongs to A” is: With consideration of the transaction value in the OnlyLastValue system the reputation of an agent “a belongs to A” is:

61. EigenTrust System In this metric the computed reputation depends on the ratings, the reputation of the raters, the transaction context (e.g. transaction value), and some community properties Ratings are p: A × E -> {−1,1} First we have to build a reputation matrix M, where (mij) contains the standardized sum of ratings from Agent i for Agent j:

62. EigenTrust System

64. New Metric We can combine different metrics to compensate for the individual weaknesses. Both Average and OnlyLast systems can be understood as summing up the previous ratings of an agent using different weights. The Blurred-system is somewhere in between, but could not handle the disturbing agents.

65. New Metric Thus we can interpolate between the Average and the OnlyLast-system by weighting the ratings not linear, like we did in the Blurred-system, but quadratic, so that the recent ratings have more influence on the reputation. The resulting metric M= (p, r) is: p: A × E -> {−1, 0, 1}

66. New Metric We call this metric a BlurredSquared System This system is invulnerable to disturbing, evil, and selfish agents. It resists malicious agent up to an amount of 60%.

67. 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

68. 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

69. 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

70. 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

71. 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

72. 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

73. 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 http://csdl2.computer.org/comp/proceedings/hicss/2002/1435/07/14350188.pdf [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, http://www.ipd.uka.de/~nimis/publications/ap2pc04.pdf 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

74. 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

75. 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

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