Trust Systems for E-Businesspaginas.fe.up.pt/~eol/TNE/APONT/CTR.pdf · seller’s feedback that indicate the following: • the abilityto fulfil positive evaluations and ship orders

Trust Systems for E BusinessTrust Systems for E-Business

Joana [email protected]

Joana Urbano, April 2010 1

Trust Systems for E-Business

• Schedule:– Trust as an Enabler Technology

– Simple Trust Systems in the Internet

– Research on Trust Systems• Traditional CTR Systems

• Situation‐aware Trust

• Other Advanced Topics

LIACC CTR System– LIACC CTR System


Trust as an Enabler Technology

• Trust seen as an enabler technology for the automatization & virtualization of business and social processesvirtualization of business and social processes– Online product buying/selling, auctions (B2C, C2C, C2B)

– Selection of business partners (e‐procurement, e‐sourcing, virtualSelection of business partners (e procurement, e sourcing, virtual organizations)

– Automatic formation of contracts /sanctions in electronic i tenvironments

– Any form of colaborative and cooperative work (e.g. task allocation)

• It has received attention from national and EU funding:– eRep, eTrustCom, new FCT project at LIACC


What is Trust

• A possible definition:– Trust is a belief that a trustier agent has in the ability and the willingness of a f g y g

trustee agent in performing a required task

• Some properties of Trust: – It depends on the evaluatorIt depends on the evaluator

• I trust X as a vendor but Y does not– It depends on the role

• I trust X as a vendor but not as a buyery– It depends on context

• I trust X as an exporter of cotton to Europe but not as an exporter of chiffon to the USA

– It depends on time• I used to trust X, but i do not trust it anymore

– It does not depend necessarily on reputationI t t X lth h it i t ll t d• I trust X, although it is not well reputed


Trust Information Sources

• From where to consider the trust evidences:– Direct experience: an appreciation of the trustee agent after having– Direct experience: an appreciation of the trustee agent after having

transacted with it

– Shared image: an appreciation of the trustee given from transmitted b h h h d i h hby another agent that has transacted with the trustee

– Reputation: a social evaluation of the trustee

Certificates and recommendations– Certificates and recommendations

– Roles in an institution

– Outcomes from contractual historiesOutcomes from contractual histories

– Rules defined in the community

– Group trustp


Simple Trust Systems in the InternetSimple Trust Systems in the Internet

Amazon.com (B2C)Amazon Marketplace (C2C)Amazon Marketplace (C2C)

eBay.com (C2C)Epinions com (C2B)Epinions.com (C2B)


Trust in Amazon – Products


five star classification

Trust in Amazon – Products


Trust in Amazon – Reviewers

A reviewer's rank is determined by the overall helpfulness of all their reviews, factoring in the number of reviews they have written More weight is given to recent reviews


number of reviews they have written. More weight is given to recent reviews

Trust in Amazon – Sellers


Trust in Amazon – Sellers

Amazon TipsLook for clues within a

stars

Look for clues within a seller’s feedback that indicate the following: • the ability to fulfil

positiveevaluations

the ability to fulfil and ship orders in a timely fashion

• a seller’s willingnessa seller s willingness to resolve transaction disputes

• an indication that

written feedback

an indication that the quality of the products shipped matches the description supplied by the seller


Trust in Amazon – Semantics and Aggregation

• Semantics of the evaluations:– 4 to 5 stars: positive feedback4 to 5 stars: positive feedback– 3 stars: neutral feedback– 1 to 2 stars: negative feedback

• Aggregation of the evaluations:– Percentages (30, 90, 365 days and lifetime)

• Σ (positive evaluations) / Σ (total evaluations)– Stars:

• Σ (star values) / Σ (total evaluations)• > 4.75 → 5 stars• between 4.26 and 4.75 → 4.5 stars• between 3.75 and 4.25 → 4 stars .........


Trust in eBay


Trust in eBay – eBay Buyer Protection


Trust in eBay – Semantics and Aggregation

• Semantics of evaluations:– Positive feedback ( )Positive feedback ( )– Neutral feedback ( )

– Negative feedback (–)

• Aggregation of evaluations:– Percentages– Percentages

• Σ (positive evaluations) / Σ (positive and negative evaluations)

• In the last 12 months, excluding feedback from the same user in the same kweek


Trust in eBay – Some Considerations

• Users are evaluated in both roles: buyer and seller

• Neutral evaluations are not taken into consideration in aggregation since• Neutral evaluations are not taken into consideration in aggregation since 2008, as they were use to prevent retalliation from sellers

• Bidders may purchase and item and leave feedback without completing y p p gthe transaction

• eBay foruns present retalliation cases:Ph ll h b l ti f db k– Phone call when buyer leave negative feedback

– Buyers bid on seller itens to leave negative feedback

• The evaluation scheeme is too simple, and few negative classifications remove a seller from business


Trust in Epinions – ReviewersReviewers earn credits (eRoyalties) for each review


Trust in Epinions – Productsproduct rating

product reviews reviewer rating


Trust in Epinions – Reviewers

review ratingg

web of trust


Trust in Epinions – Reviewers


Trust in Epinions – Some Considerations

• Product rating:– The aggregation of evaluations is a weighted means of the evidences– The aggregation of evaluations is a weighted means of the evidences

(stars), weighed by:• The quality of the reviews• The recency of the reviews y• The number of the reviews

• Reviews rating:– Not Helpful, Somewhat Helpful, Helpful, Very Helpful, Most Helpful

(only advisors and category Leads), Off Topic

• It uses the concept of web of trust• It uses the concept of web of trust– Reproduces the concept of word‐of‐mouth– Network of reviewers whom evaluations and reviews we consider

more useful– The ones that trust me can inherit choices of my web of trust (a

problem?)


Research on Trust Systems

Traditional CTR SystemsTraditional CTR SystemsSituation‐Aware TrustOther Advanced TopicsOt e d a ced op cs


Traditional Trust Systems

• Research questions addressed:– Theorethical models of trust and reputation

– Representation and aggregation of information

• Issues in consideration:I f ti– Information sources

– Architecture

R t ti f t t id– Representation of trust evidences

– Aggregation engines

i i f d– Protection against fraud



• Information Sources– The most common type of evidence is the direct experience (can include contractual history)

– … followed by reputation, normally represented by network graphs• One research question arise: is trust transitive? (Christianson and Harbison, 1997)

Shared images (Sabater and Paolucci 2007)– Shared images (Sabater and Paolucci, 2007)

– Very few work on practical implementations of certificates recommendations roles and rulescertificates, recommendations, roles and rules



• Architecture– Centralized vs. decentralized

• Representation of trust outcomesRepresentation of trust outcomes– Symbolic: e.g. {‐, neutral, +}, stars

– Boolean: true / falseBoolean: true / false

– Continuous: e.g. [0, 1]

– Categories: e g bad ok goodCategories: e.g. bad, ok, good

– Fuzzy labels: e.g. [vb, b, o, vg, confidence]



• Aggregation enginesS i ht d (H h 2006)– Sum, mean, weighted mean (Huynh, 2006)

– Beta distributions: (n+1) / (N+2)

– Dirichlet distributions: beta distributions for each dimension of– Dirichlet distributions: beta distributions for each dimension of evidence and correlation of dimensions

– Naïve Bayesian networks: P(Y|X) = (P(X|Y).P(Y)) / P(X)

– Heuristics (e.g. using dynamics of trust) (Urbano, 2009)

– Work done at LIACC have shown than engines that embed the dynamics of trust get better results than engines that uses weighted


y g g gmeans (Danek, 2010)


• Dealing with deception and trust fraud– Deceive in big commitments after gaining reputation with several small commitments

h f d– Change of identity

– Leave false feedback or no feedback

– Fake transactions

– Collude



• Some well known models:– Repage (Paolluci and Sabater, 2007)

– Fire (Huynh, Jennings, Shadbolt, 2006)

– Jonker and Treur model with dynamics of trust (1999)

– Beta Reputation System (Josang, Ismail, 2002)

– Hystos (Zacharia and Maes, 2000)



• What traditional trust systems lack:– Situation‐aware trust

– Aggregation of heterogeneous contracts

– Dealing with newcomers


Situation-Aware Trust

• Trust is Situational– I may trust my brother to drive me to the airport, I most certainly

would not trust him to fly the plane (Marsh, 1994)

– A person trusting Bob as a good car mechanic will not automatically trust him also in undertaking heart surgeries (…) [but] he probably could be quite good in repairing motorcycles (Tavakolifard, 2009)

– A high tech company may fear to select a partner from a country of origin without high technology tradition, even though this partner has proved high quality work in the desired task in the recent past (Urbano, 2009)


Situation-Aware Trust

• There are few implemented proposes on this area:T k lif d (2009)– Tavakolifard (2009)

– Rehak, Gregor, and Pechoucek (2008)

– Hermoso Billhardt Ossowki (2009)Hermoso, Billhardt, Ossowki (2009)

– Angela Fabregues et al. (2009)

– Rettinger, Nickles, and Tresp (2008, 2009)g p ( )

– Neisse et al. (2008)

• The majority of them is based on ontology‐like models and• The majority of them is based on ontology‐like models and rely on pre‐established similarity measures


Example of SAT Models – Tavakolifard 2009

• Trust relations in one domain are used to infer trust relations in similar domains

• An ontology allows the representation of the similarity between domains

• A situation is represented as a set of contextsas a set of contexts

• Each context has a set of local contexts (aspects)

• The similarity between situations is a weighted sum of the similarity bbetween contexts

• The similarity between contexts is a weighted sum of the similarity between local contexts


Example of SAT Models – Rehak et al. 2008• Represents a situation as a context, i.e. a point ci in the context space C

• The context space is a Q‐dimensional metric space with one dimension h d i i fper each represented situation feature

• The metrics d(c1, c2) defined on C describe the similarityon C describe the similarity between the contexts c1 and c2

• Trustworthiness values are associated to a set R of reference contexts ri


Other Advanced Topics

• How to aggregate heterogeneous evidences– Problem of missing attributes

– Problem of different semantics

• How to deal with newcomers– For each there is no evidences?

– Risk, exclude, or estimate trustworthiness based on organizational contextual similarities?


LIACC CTR System

SinAlpha Aggregation EngineSinAlpha Aggregation EngineContextual Fitness

Future Workutu e o


LIACC CTR System

CTR Engine

tune

Similarity

Analyzer

tune

SinAlphaContextual

Fitness


The SinAlpha Aggregation Engine

y(α) = δ sin α + δ

Hysteresis of Trust and Betrayal (Straker, 2008)

AsymmetryTrust is hard to gain and easy to loose

y(α) = δ.sin α + δ,α0 = 3π/2,α = α + λ . ω

MaturityTh l f t t thThe slope of trust growth varies with the trustworthiness stage of the agent

Distinguishable PastDistinguishes between different patterns of past behaviour


maturityasymmetry

Experiments with SinAlpha


Contextual Fitness

• Dynamic, incremental extraction of tendencies of f l ffailure of trustee agents

• Current necessity is compared to failure tendencies

Tendency for trustee agent: null, null, low, false

C CFP hiff 1080000 7Current CFP: chiffon, 1080000, 7

There is a match!

• Global trustworthiness is computed accordingly


Contextual Fitness – Extraction of Tendencies

• Using Paliouras et al. (1999) clustering‐based metric:

• Another approach (submitted)


Experiments with Contextual Fitness

Utility gain by different approaches graph (submitted)


Future Work

• Extraction of tendencies with heterogeneous devidences

• Experimenting multi‐dimensional outcomes

• Make the bridge to normative environments

• Deal with newcomers & argumentationDeal with newcomers & argumentation

A d l ( ?)• And also (anyone?):– Explore disseminated trust information in the Internet

– Categorize trustees to deal with newcomers

– Systematize reputation


People on Trust Module

• Joana Urbano (PhD student)

• Ana Paula Rocha and Eugénio Oliveira (Coordinators)

• Past collaboration:– Agnieszka Danek (3 month grant)– Agnieszka Danek (3 month grant)

– Filipe Silva (MIEIC dissertation)


Thank you!([email protected])


Documents

Trust Systems for E-Businesspaginas.fe.up.pt/~eol/TNE/APONT/CTR.pdf · seller’s feedback that indicate the following: • the abilityto fulfil positive evaluations and ship orders