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Amirreza Masoumzadeh

March 25, 2008

Social Web @ SIS . Pitt

Agenda� Definitions

� Properties

� Trust in Information Security

� Reputation and Collaborative Filtering

� Trust/Reputation Systems

� Trust/Reputation Computation

� Applications� Commercial online systems

� More theoretical ones

� Problems in Online Systems

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Reputation� [OED]

� Common or general estimate of a person with respect to character or other qualities

� [Wikipedia]

� Opinion (more technically, a social evaluation) of the public toward a person, a group of people, or an organization

Trust� [OED]

� Confidence in or reliance on some quality or attribute of a person or thing, or the truth of a statement

� Reliability Trust [Gambetta]� Subjective probability by which an individual, A, expects that

another individual, B, performs a given action on which its welfare depends

� Decision Trust [McKnight and Chervany]� The extent to which one party is willing to depend on

something or somebody in a given situation with a feeling of relative security, even though negative consequences are possible

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Reputation vs. Trust� Have been used interchangeably by some authors

� A simple example

� I trust you because of your good reputation

� I trust you despite your bad reputation

Other Trust Properties� Context dependant

� Trusting a doctor for recommending a medicine vs. a bottle of wine!

� Trusting an old-looking rope for climbing down of an apartment when in a fire drill vs. a real fire situation

� Transitive

� Not completely in mathematical sense, but it is passed

� Asymmetric

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Trust Transitivity

Trust measures� Binary

� e.g., “Trusted”, “Not trusted”

� Discrete

� e.g., strong-trust, weak-trust, weak-distrust, strong-distrust

� Continuous

� Percentage

� Probability

� Belief

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Semantics of measures� Specificity/generality

� Specific: relates to a specific trust aspect

� General: represent an average of all aspects

� Subjectivity/objectivity

� Subjective: subjective judgment

� Objective: assigned based on formal criteria

Specific General

Subjective Survey questionnaires eBay, Voting

Objective Product tests Synthesized general score from product tests

Semantics of measures:

Characteristics� Subjective

� Difficult to protect against unfair ratings

� Objective

� Verifiable by others

� Generated automatically

� Subjective-general

� often fails to assign a credit or blame to the right aspect or even the right party

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Trust in Information Security� Identity trust

� Measure of correctness of a claimed

� Measured using credentials

� Term trust provider is used for CA

� Trust negotiation

� Chained identity certificates : trust transitivity

� Distributed trust management

Soft Security� Traditional security mechanisms protect resources

from malicious users using policies (hard security)

� Users have to protect themselves from those who offer services (reverse scenario)� E.g., false or misleading information

� Trust and reputation systems as social and collaborative control mechanisms

� Assessing the behavior of members in the community against the ethical norms� Ethical norms are not defined precisely, but dynamically

formed

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Collaborative Filtering vs.

Reputation SystemsCollaborative Filtering Collaborative Sanctioning

� Collect ratings

� Subject to taste as input

� Different people, different tastes

� Find neighbors

� Goal

� Better recommendations to users

� Optimistic world view

� All participants are trustworthy and sincere

� Collect ratings

� Insensitive to taste

� All the community have similar opinion (if they are truly aware)

� Goal

� Sanction poor service providers, motivating them to provide quality services

� Pessimistic world view

� Some participants try to misrepresent

� Some works study the effect of reputation on collaborative filtering

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Traditional vs. Online Environments� Traditional cues of trust and reputation (e.g., physical

encounter) are missing in online environments

� Find additional online substitutions

� Communicating and sharing trust and reputation info is relatively difficult in real world and normally considered to local communities in the physical world

� Take advantage of IT and Internet

Properties of Reputation Systems� Entities must be long lived, so that with every

interaction there is always an expectation of future interactions

� Ratings about current interactions are captured and distributed

� Protocol: centralized is easy, distributed is challenge

� Willingness to rate

� Ratings about past interactions must guide decisions about current interactions

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Trust vs. Reputation Systems (I)Trust Systems Reputation Systems

� Input

� take subjective and general measures of (reliability) trust into account

� Return value

� a score reflecting the relying party's subjective view of entity's trustworthiness

� Personalized

� Input

� use ratings about specific (and objective) events, such as transaction

� Return value

� entity's (public) score as seen by the whole community

� Global

Trust vs. Reputation Systems (II)Trust Systems Reputation Systems

� Appropriate for

� Medium and small environments

� Transitivity

� an explicit component

� Appropriate for

� Large environments Such as online reputation systems

� Transitivity

� take it into account implicitly

� Not always clear to classify a system as one of them!

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Centralized Architecture� Centralized

communication protocol� Participants provide

ratings about transaction partners

� Obtain reputation scores from CA

� Reputation computation engine� CA use it to derive

reputation scores based on ratings and possibly other info

Distributed Architecture� No CA, but distributed

stores or just each participant records its own ratings

� Relying party must find distributed stores or get as many as possible ratings from previous partners of the entity

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Information Sources� Direct experience

� Most relevant and reliable

� Witness information (recommendations)

� Sociological information

� Different type of relations between society members based on roles individuals play

� Prejudice

� Assign properties to an individual based on signs that identify it as a member of a given group

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Simple Summation/Average� Summation

� Binary rating (positive/negative)

� Very simple to understand, primitive

� Average

� Numerical ratings

� Weighted average

� Factors such as rater trustworthiness/reputation, age of the rating, etc.

Discrete Trust Models� Discrete verbal statements for ratings instead of

continuous measures

� [Abdul-Rahman et al]

� Very Trustworthy, Trustworthy, Untrustworthy, Very Untrustworthy

� Lookup tables with entries for referred trust and referring party upgrade/downgrade

� No sound computational principles, instead heuristic mechanisms

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Bayesian Systems� Binary rating (positive/negative)� Beta PDF parameter tuple (a,b)

� a: amount of positive observations + 1� b: amount of negative observations + 1

� E(p) = a/(a+b)� Reputation is computed by statically updating beta PDF� Theoretically sound basis, too complex for average person

Belief Models� Belief theory

� Sum of probabilities over all possible outcomes not necessarily add up to 1

� Uncertainty (remaining probability)

� [Jøsang, 1999, 2001]� Belief/trust metric to express trust referrals

� Subjective logic� Discounting operator� Consensus operator

� Demo: http://sky.fit.qut.edu.au/~josang/sl/

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Fuzzy Models� Linguistically fuzzy concepts

� Membership functions describe the degree of trustworthiness

Flow Models� Compute trust by transitive iteration through looped

or arbitrary long chains

� Usually constant trust/reputation weight for the whole community

� Increase is done at the cost of the others (normlization)

� E.g., PageRank, Appleseed, Advogato

� Score increase: incoming flow

� Score decrease: outgoing flow

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Web-Based Social Networks� Users explicitly express their relationships using built-in support by the

systems� Publicly available data that form a web of trust� Inferring trust in WBSN to integrate into applications [Golbeck 2006]

� Binary-value network� Infer trust of a source node in a sink node� Uses BFS and rounding average

� Show that accuracy is high for� g×pa > 0.5

Some Commercial and Live Reputation Systems

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eBay’s Feedback Forum (I)� Sellers and buyers rate each other (Positive/neutral/negative), Summation scoring

�

eBay’s Feedback Forum (II)� Ratings statistics [Resnick et al, 2002]

� 51.7 % of buyers and 60.6 % of sellers provide ratings

� Negative < 1 %, neutral < 0.5 %, positive ≈ 99 %!

� Very primitive, but works well� Serious sellers don’t want negative feedbacks

� Threat of negative feedback works better in favor of customer than actual negative feedback

� Ballot stuffing is a minor problem� Rating is only allowed after the completion of a transaction

� Fake transactions: eBay charges a fee for listing items

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Epinions� Review for consumer products� Product rating

� 1 to 5 stars + comment� Average scoring

� Review rating� Not helpful, somewhat helpful, helpful, very helpful� Average scoring

� Reviewer status� Member, advisor, top reviewer, category lead

� Income share program� Gives cash to reviewers with high number of very helpful

reviews

Epinions: Product Review

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Epinions: User Profile

Amazon� Different categories of reputation� Review product

� Logged-in users� Rating: 1 to 5 stars + comment� Average scoring

� Review ratings� Logged-in users� Helpful or not helpful� Reviewer score: number of “helpful” ratings

� Review seller/buyer� After transaction� Rating: 1 to 5 stars (positive, neutral, and negative) + comment� Average scoring

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Amazon: Product Rating

Amazon: Product Reviews

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Amazon: Buyer Feedback

Expert Sites� AllExperts

� A pool of individuals to answer questions in their area of expertise

� Rating [1,10] on knowledge, clarity, timeliness, and politeness aspects

� Score� Numerical average of ratings for each aspect

� Sum of all scores

� Number of answered questions

� Advogato� Centralized, Flow model

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More Theoretical Applications

Google’s PageRank� Rank pages based on a page’s reputation

� Calculate page reputation in an iterative process

� Each incoming link adds to the reputation by the amount of reputation of the source divided by the number of the nodes it points to

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Reputation in P2P Networks� Search phase

� central / distributed (pure P2P) / Fasttrack (node and supernode)

� RS can help to identify� Reliable resource providers

� Reliable servents recommenders

� RS can fight problems such as� Spreading malicious software

� Free riding

� Content poisoning

EigenTrust� Calculates a global reputation vector in P2P systems

� Each peer i calculates local trust value for peer j

� Trust values are aggregated based on the idea of transitive trust

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TrustMail� Based on trust inference in WBSN� Filter email messages according to the trust value of the sender� Expect high coverage if users rate the people to whom they send

messages

TrustFilm� Based on trust inference in WBSN

� Weighted average of everyone’s rating based on their trust value

Minimum difference between known user rating and average rating

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Low Incentive for Providing Ratings� Problems like Free-riding could exist

� Some schemes exist that provide financial rewards for honest ratings

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Bias Toward Positive Rating� Exchange of courtesies

� Hope of getting positive in return

� Fear of retaliation

� Obvious solution: anonymous review

Unfair RatingsEndogenous discounting Exogenous discounting

� Comparing with the rating values themselves

� Assumption: unfair ratings can be recognized by their statistical properties

� Externally determined reputation

� Assumption: raters with low reputation are likely to give unfair ratings and vice versa

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Change of Identities� Assumption was that identities are long lived

� Penalize newcomers

� Difficult to distinguish between bad and good newcomer

Quality Variations Over Time� Discounting of the past

� Forgetting factor, aging factor, fading factor

� Longevity factor

� Can be a function of time or frequency of the transactions or both

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Discrimination� In service

� Good service for all but one

� Endogenous discounting can give false positive

� In ratings

� Equals to unfair ratings

Ballot Box Stuffing� More than legitimate number of ratings is provided

� eBay seems to provide good protection: only after transaction completion

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Thanks for your attention.

Questions?