The Beta Reputation System - PowerPoint Presentation

Slide1

The Beta Reputation System

Audun

Jøsang

and

Roslan

Ismail

[1]

Presented by

Hamid

Al-

Hamadi

CS 5984, Trust and Security, Spring 2014Slide2

Outline

Introduction

Building Blocks in the Beta Reputation System

Performance of the Beta Reputation SystemConclusion

2Slide3

Introduction

Many existing reputation systems

Applicability in e-commerce systems:

Enforcement is needed in order for contracts and agreements to be respectedTraditionally rely on legal procedures to rectify disagreement.Hard to enforce in e-commerce

unclear which jurisdiction applies

cost of legal procedures

3Slide4

Introduction

Reputation systems

As a substitute to traditional Reputation systems can be used to encourage good behavior and adherence to contracts

Fostering trust amongst strangers in e-commerce transactions Gathers, distributes, and aggregates feedback about participants behavior Incentive for honest behavior and help people make decisions about who to trust.

Without a reputation system taking account past experiences, strangers might prefer to act deceptively for immediate gain instead of behaving honestly.

4Slide5

Introduction

Online auction sites were the first to introduce reputation schemes e.g. eBay.com

Others include company reputation rating sites such as BizRate.com, which ranks merchants on the basis of customer ratings

The internet is efficient in capturing and distributing feedback, unlike the physical world. Some challenges: An entity can attempt to change its identity to erase prior Feedback

Restart after it builds a bad reputation

Not enough feedback provided by surrounding entities

Negative feedback hard to elicit

Difficult to ensure feedback is honest

5Slide6

Introduction

Example of dishonesty through reputation systems:

Three men attempt to sell a fake painting on eBay for $US135,805

Two of the fraudsters actually had good Feedback Forum ratings as they rated each other favorably and engaged in honest sales prior to fraudulent attempt. Sale was abandoned just prior to purchase, buyer became suspicious

6Slide7

Introduction

Fundamental aspects:

Reputation engine

Calculates users’ reputation ratings are from various inputs including feedback from other users Simple or complex mathematical operationsPropagation mechanism

Allows entities to obtain reputation values

Two approaches:

Centralized (e.g. eBay)Reputation values are stored in a central server

Users forward their query to the central server for the reputation value whenever there is a need

Decentralized

Everybody keeps and manages reputation of other people themselves

Users can ask others for the required reputation values

7Slide8

Introduction

Authors propose a new reputation engine based on the beta probability density function called the

beta reputation system

strongly based on theory of statistics paper describes centralized approach, but the reputation system can also be used in a distributed setting

8Slide9

Building Blocks in the Beta Reputation System

The Beta Density Function

Can be used to represent probability distributions of binary events

The beta-family of probability density functions is a continuous family of functions indexed by the two parameters α and

β

.

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Building Blocks in the Beta Reputation System

“When observing binary processes with two possible outcomes , the beta function takes the integer number of past observations of and to estimate the probability of , or in other words, to predict the expected relative frequency with which will happen in the future.”

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Building Blocks in the Beta Reputation System

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Building Blocks in the Beta Reputation System

Example:

process with two possible outcomes

Produced outcome 7 times Produced outcome 1 timeWill have beta function as plotted below:

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Building Blocks in the Beta Reputation System

Example (cont’):

Curve represents the uncertain probability that the

process will produce outcome

during future observations

probability expectation value

-> the most likely value of the

relative frequency of outcome

is 0.8

8 / (8 + 2)

represents the probability of an event

represents the

probability that the first-order variable has a specific value

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Building Blocks in the Beta Reputation System

The Reputation Function

In e-commerce an agent’s perceived satisfaction after a transaction is not binary - not the same as statistical observations of a binary event.

Let positive and negative feedbacks be given as a pair of continuous values.

Degree of satisfaction

Degree of dissatisfaction

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Building Blocks in the Beta Reputation System

Compact notation :

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Building Blocks in the Beta Reputation System

T’s reputation function by X is subjective (as seen by X)

Superscript (X): feedback provider

Subscript (T): feedback target

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Building Blocks in the Beta Reputation System

The Reputation Rating

Simpler representation to communicate to humans that a reputation function

Given as a probability value – within a rangeNeutral value is in middle of range

Scale the rating to be in the range [-1,+1]

A measure of reputation and how an entity is expected to behave in the future

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Building Blocks in the Beta Reputation System

Combining Feedback

Can combine positive and negative feedback from multiple sources e.g. combine feedback from X and Y about target T

Combine positive feedback

Combine negative feedback

Operation is both commutative and associative

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Building Blocks in the Beta Reputation System

Discounting

Used to vary the weight of the feedback based on the agents reputation

Described in the context of belief theoryJøsang’s belief model uses a metric called opinion to describe beliefs about the truth of statements

interpreted as probability that proposition x is true

interpreted as probability that proposition x is false

interpreted as inability to assess the probability value of x

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Building Blocks in the Beta Reputation System

Y has opinion about T, gives it to X

X has opinion about Y

Then X can express its opinion about T taking into account its opinion about Y’s advice

, as follows:

Given by Y (its opinion about T)

Apply X’s opinion

about Y

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Building Blocks in the Beta Reputation System

The opinion metric can be interpreted equivalently to the beta function

mapping between the two representations defined by:

Using previous eq., discounting operator for reputation functions is obtained:

Associative but not

commutative

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Building Blocks in the Beta Reputation System

Forgetting

Old feedback less relevant for actual reputation rating

Behavior changes over time Old feedback is given less weight than new feedback Can use an adjustable forgetting factor

If

λ

=1 -> no forgetting factor, nothing is forgotten

If

λ

=0 -> only last feedback, all others forgotten

Order of feedback

processing matters

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Building Blocks in the Beta Reputation System

Forgetting (cont’)

To avoid saving all of the feedback

tuples (Q) forever, a recursive algorithm can be used instead:

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Building Blocks in the Beta Reputation System

Providing and collecting feedback:

After each transaction, a single agent can provide both positive

and negative feedback simultaneously: Feedback can be partly satisfactory, and given as a pair The sum can be interpreted as the weight of the feedback

Minimum weight of feedback is r + s = 0, equivalent to not providing

feedback

Alternatively, define a normalization weight denoted by so that the sum of the parameters satisfy

Feedback can be provided as a single value with values within a specified

range

If we have such that then the can be derived

using and as follows:

Weight can reflect importance of transactions (high importance -> high )

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Building Blocks in the Beta Reputation System

Feedback is received and stored by a feedback collection centre

C

Assumed that all agents are authenticated and that no agent can change identity Agents provide feedback about transaction C

discounts received feedback based on providers reputation and updates the

target’s reputation function and rating accordingly

C provides updated reputation ratings to requesting entities

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Performance

Example A: Varying Weight

This example shows how the reputation rating evolves as a function

of accumulated positive feedback with varying weight w

Let C receive a sequence Q of n identical feedback values

v=1

about target T Then:

Reputation rating:

Reputation parameters:

Derived from previous equations:

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Performance

w=1

w=0

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Performance

Example B: Varying Feedback

This example shows how the reputation rating evolves as a function of accumulated feedback with fixed weight

w = 1 and varying feedback value v

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For v=1 the rating approaches 1,

and for v=-1 the rating

approaches -1.

V=1

V=-1Slide29

Performance

Example C: Varying Discounting

This example shows how the reputation rating evolves as a function of accumulated feedback with fixed weight

w = 1 and varying discounting C receives a sequence Q of n

identical feedback values

v =1

about target T Forgetting is not considered Each feedback tuple

with fixed value (1, 0) is discounted based on the feedback provider’s reputation function defined by

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Reputation rating:

Reputation parameters:Slide30

Performance

Example C: Varying Discounting (cont’)

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Varying Feedback provider’s

reputation function parameters

practically equivalent

to no discounting at all

As X’s reputation function gets weaker T’s rating is less influenced by the feedback From X

with r=0, s=0 , T’s rating not influenced by X’s ratingSlide31

Performance

Example D: Varying Forgetting Factor

This example shows how the reputation rating evolves as a function of accumulated feedback with fixed weight

w = 1 and varying forgetting factor λ C receives a sequence

Q

of

n identical feedback values v =1 about target T Discounting is not considered Using previous equations, the reputation parameters and rating can be expressed as a function of

n

and

λ

according to:

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Performance

Example D: Varying Forgetting Factor (cont’)

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Performance

Example E: Varying Feedback and Forgetting Factor

This example shows how the reputation rating evolves as a function of accumulated feedback with fixed weight

w = 1. Let there be a sequence Q of 50 feedback inputs about T

, where the first 25 have value , and the subsequent 25 inputs have value

Using previous equations, the reputation parameters and rating can be expressed as a function of

n, v, and λ according to:

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In more

explicit

form:Slide34

Performance

Example E: Varying Feedback and Forgetting Factor (cont’)

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In more explicit form:Slide35

Performance

Example E: Varying Feedback and Forgetting Factor (cont’)

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Two phenomena can be observed when the forgetting factor is low (i.e. when feedback is quickly forgotten):

Firstly the reputation rating reaches a stable value more quickly, and

secondly the less extreme the stable reputation rating becomes.

v

=1

v

=-1Slide36

Conclusion

Reputation systems can be used to encourage good behavior and adherence to contracts in e-commerce

Authors propose a beta reputation system which is based on using beta probability density functions to combine feedback and derive reputation ratings

Strong foundation on the theory of statisticsAssumed a centralized approach, although it is possible to adapt the beta reputation system in order to become decentralized

flexibility and simplicity makes it suitable for supporting electronic contracts and for building trust between players in e-commerce

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References

[1] A.

Josang

, and R. Ismail, "The Beta Reputation System,” 15th Bled Electronic Commerce Conference, Bled, Slovenia, June 2002, pp. 1-14.37