polaritytrust: measuring trust and reputation in social networks

Departamento deLenguajes y Sistemas Informáticos

escuela técnica superiorde ingeniería informática

PolarityTrust: measuring Trust and Reputation in Social Networks

F. Javier Ortegajavierortega@us.esJosé A. Troyano

troyano@us.esFermín L. Cruz

fcruz@us.esFernando Enríquez de Salamanca

fenros@us.es

Motivation

♦ Example: on-line marketplaces

Motivation

Motivation

How can I choose the best seller?

The one with highest amount of sales?The one with most positive opinions?The cheapest one?

How can I make the most from these transactions?

Selling more products but cheaper?Selling rare (and maybe expensive) articles?Free shipping?

Motivation

♦ Δ Reputation => Δ Sales

♦ Gaining high reputation:

● Obtain (false) positive opinions from other accounts (not neccesarily other users).

● Sell some bargains to obtain high reputation from the buyers.

● Give negative opinions for sellers that can be competitors.

Motivation

♦ Goals:● Compute a ranking of users according to their

trustworthiness

● Process a network with positive and negative links (opinions) between the nodes (users)

● Avoid the effects of the actions performed by malicious users in order to increase their reputation

Roadmap

♦ Introduction

♦ PolarityTrust

♦ Evaluation

♦ Conclusions

Introduction

♦ Trust and Reputation Systems (TRS) manage trustworthiness of users in social networks.

♦ Common mechanisms:● Moderators (on-line forums)● Votes from users to users (eBay)● Karma (Slashdot, Meneame)● Graph-based ranking algorithms (EigenTrust)

Introduction

♦ Users feedback needed!

♦ Problems:● Positive bias● Incentives for users feedback● Cold-start problem● Exit problem● Duplicity of identities

Introduction

♦ Malicious users strategies to gain high reputation:♦ Orchestrated attacks: Obtaining positive

opinions from other accounts (not neccesarily other users).

♦ Camouflage behind good behavior: selling some bargains to obtain high reputation from the buyers.

♦ Malicious spies: using a honest account to provide positive opinions to a malicious user.

♦ Camouflage behind judgments: giving negative opinions from seller that can be competitors.

Introduction

♦ Malicious users strategies to gain high reputation:♦ Orchestrated attacks: Obtaining positive

opinions from other accounts (not neccesarily other users).

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Introduction

♦ Malicious users strategies to gain high reputation:♦ Camouflage behind good behavior: selling

some bargains to obtain high reputation from the buyers.

8

9

67

3

2

54

0

1

Introduction

♦ Malicious users strategies to gain high reputation:♦ Malicious spies: using a honest account to

provide positive opinions to a malicious user.

8

9

67

3

2

54

0

1

Introduction

♦ Malicious users strategies to gain high reputation:♦ Camouflage behind judgments: giving

negative opinions from seller that can be competitors.

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3

2

54

0

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PolarityTrust

♦ Graph-based ranking algorithm

♦ Two scores for each node: PT and PT⁺ ⁻

♦ Propagation of trust and distrust over the network

♦ PT and PT influence each other depending on the ⁺ ⁻polarity of the links between a node and its neighbours.

PolarityTrust

♦ Propagation mechanism:● Given a set of trustworthy users● Their PT and PT scores are propagated to their ⁺ ⁻

neighbours, and so on.

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PolarityTrust

♦ Propagation rules:● Positive opinions => direct relation between scores● Negative opinions => cross relation between scores

a

b

c

a

b

c

♦ Non-negative Propagation extension:● Avoid the propagation of negative opinions from negative

users

Evaluation

♦ Baselines:● EigenTrust● Fans Minus Freaks

♦ Evaluation metrics:● Number of inversions: bad users in good positions● Incremental number of bad nodes

♦ Dataset:● Randomly generated graphs: Barabasi and Albert model.● Malicious users added in order to perform common attacks

Evaluation

♦ Performance against common attacks:

Models ET FmF PT PT+NN

A 50 0 0 0

B 197 36 0 0

C 63 207 94 94

D 86 9 9 9

E 74 4 0 0

Models ET FmF PT PT+NN

A 50 0 0 0

B 197 36 0 0

B+C 155 873 27 27

B+C+D 169 871 26 26

B+C+D+E 183 849 38 36

A: No attacks

B: Orchestrated attacks

C: Camouflage behind good behaviour

D: Malicious Spies

E: Camouflage behind judgments

Evaluation

♦ Performance against incremental number of malicious users:

Conclusions

♦ Something