Reputation

Prakash KolanLiqin Zhang

Venkatesh Kancherla

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.

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

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

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

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

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].

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].

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

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

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.

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.

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

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.

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

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

Reputation Systems

Notions of Reputation

Reputation Typology

Reputation can be viewed as a global or personalized quantity[15]

Notions of Reputation

Individual & Group reputation

Reputation is a function of thecumulative 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

Notions of Reputation

Direct & Indirect( Individual) reputation

Reputation estimates by an evaluator based on direct

experiences (seenor experienced by the

evaluating agent first hand)

Reputation estimates that are based on second-hand evidence (such as by word-

of-mouth).

Notions of Reputation

Direct Reputation

Reputation based on actual encounter with the

reputed agent

Reputation based on evaluator’s rating for

the reputed agent

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

Requirements

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.

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.

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

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.

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.

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.

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

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.

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.

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.

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: Qebay = {−1, 0, 1}

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.

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,tnow)

A complete Metric M is defined as

M=(p,r,Q,R,r0)

METRICS IN REPUTATION SYSTEM

Accumulative Systems Average Systems Blurred Systems OnlyLast Systems EigenTrust System

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.

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

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

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

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}

Blurred System

The reputation of an agent “a belongs to A” without considering transaction values is:

With consideration of transaction values:

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.

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:

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:

EigenTrust System

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.

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}

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%.

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, 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, 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

[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: 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

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

References

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