adaptiveness and social-compliance in trust management. a multi-agent approach

Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion

Adaptiveness and Social-Compliancein Trust Management –

a Multi-Agent Based Approach

Reda Yaich

ISCODInstitut Henri FayolEcole des Mines

Saint-Etienne

29 October 2013

1


Open and Decentralised Virtual Communities

A

D

C

G

E

B

F

H

f

Frank

George

EliseCarl

Alice

Dave

Bob

Helen

A group of people with a common purpose whose interactions aremediated and supported by computer platforms” [Preece, 2004]

2


Trust in Virtual Communities

In virtual communities, decisions made by members are risky anduncertainI Who can access my resources?I Who can join my community?

Does security help?I Resources and actors should be known !

I How much credit can I assign to the partner?I Who is the best partner I can interact with?

TrustTrust enables people to make decisions in complex environmentsbased on positive expectations [Luhmann, 1990]

3


Objectives

Design a system that assists members of open and decentralisedvirtual communities in their trust decisions

Challenging PropertiesI Openness: people can join and leave communities at willI Dynamics: ever-evolving contextI Social-Compliance: self-interests vs. collective objectivesI Decentralization: no central authority

4


1 Introduction

2 Research ScopeTrust Management LandscapeObjectives

3 Trust Management System (TMS)

4 Adaptive TMS (A-TMS)

5 Adaptive and Socially-Compliant TMS (ASC-TMS)

6 Evaluation

7 Conclusion

5


Trust in Computer Science

ACLPolicyMaker

IBM TE

STM

Rei

PGP(WoT)

ReGret FIRE LIAR

ForTrust

PROTUNE

Attributes

Trust Management Negotiation

CTM

Reputation Reliability

Deontic

hybrid

Roles

SocialTrust

Social Trust

TrustBuilder

Ponder

X.509(PKI)

SocialRelations

Trust Model

MAS

XACML1.0

XACML3.0

XACML2.0

ComputationalTrust

RT

2000 20121970 2005 20081990

ATNAC

Soft

Trus

t App

roac

hes

Har

d Tr

ust A

ppro

ache

s

6


Hard vs Soft Trust Approaches

How the challenging properties have been addressed?

Hard Trust Soft TrustOpenness Attributes ExperienceDynamics Policies Context-Awareness

Social-Compliance Integration Social Control/NormsDecentralization Delegation Multi-Agent

7


Research Objectives

Assist virtual communitymembers in their trustdecisions taking intoaccount:I Openness

I DynamicsI Social-ComplianceI Decentralization

8


Research Objectives

Assist virtual communitymembers in their trustdecisions taking intoaccount:I OpennessI Dynamics

I Social-ComplianceI Decentralization

Trust Factors Trust Policy

8


Research Objectives

Assist virtual communitymembers in their trustdecisions taking intoaccount:I OpennessI DynamicsI Social-Compliance

I Decentralization


AdaptivenessIndividual Policy Adaptation to the

Environment

Individual Policy Adaptation to the

Partner

8


Research Objectives

Assist virtual communitymembers in their trustdecisions taking intoaccount:I OpennessI DynamicsI Social-ComplianceI Decentralization Trust Factors Trust Policy


Environment


Partner

Social-Compliance

Individual Policy Adaptation to the Collective

Collective Policy Adaptation to the Individual

8


Research Objectives

Assist virtual communitymembers in their trustdecisions taking intoaccount:I OpennessI DynamicsI Social-ComplianceI Decentralization

Multi-Agent Based Trust Management System



Environment


Partner

Social-Compliance



8


1 Introduction

2 Research Scope

3 Trust ManagementSystem (TMS)

Trust FactorsOntologyFlexible PolicyLanguage


5 Adaptive andSocially-Compliant TMS(ASC-TMS)

6 Evaluation

7 Conclusion


9


Trust Factors Ontology (TFO) ∆f

A hybrid trust management approach

TrustFactor

10




TrustFactor

IndicatorProof

Subsumption

Disjonction

10




TrustFactor

IndicatorProof

Reliability

Selfishness

Experience

Reputation

Subsumption

Disjonction

10




TrustFactor

IndicatorProof

Reliability

Selfishness

Experience

ReputationDegree

Competences

Experience

Membership

Subsumption

Disjonction

10




TrustFactor

IndicatorProof

Reliability

Selfishness

Experience

ReputationDegree

Competences

Experience

Prof.

PhDMaster

Licence

Bachelor

Engineer

Membership

Subsumption

Disjonction

Is A

10




TrustFactor

IndicatorProof

Reliability

Selfishness

Experience

ReputationDegree

Competences

Experience

Prof.

PhDMaster

Licence

Bachelor

Engineer

Membership

Subsumption

Lower

Disjonction

Higher

Is A

10




TrustFactor

IndicatorProof

Reliability

Selfishness

Experience

ReputationDegree

Competences

Experience

Prof.

PhDMaster

Licence

Bachelor

Engineer

Membership

Subsumption

Lower

Equivalent

Disjonction

Higher

Is A

10


Policy Language

A policy is defined by a set of trust criteriaπPattern

Issuer = {〈f1, o1, v1,w1, t1〉, ..., 〈fn, on, vn,wn, tn〉}Where :I fi is the trust factor name (f ∈ ∆f .T )I oi is a comparison operator from (oi ∈ {>, <,≤,≥,,,=})I vi is a threshold value (vi ∈ ∆f .A )I wi is a weight value (wi ∈ Z)I ti ∈ {’m’, ’o’} specifies if the criterion is mandatory or not

Bob’s policy for the pattern 〈access, notes〉

π〈access,notes〉bob = {〈identity,≥,marginal, 2,m〉,

〈age, >, 18, 2,m〉, 〈age, <, 30, 2,m〉,

〈reputation,≥, 60%, 2, o〉,

〈recommendation,≤, 2, 1, o〉}11


Policy Evaluation

The evaluation E(πxa , ψ

b) of:The policy πx

a = {〈f1, op1, v1,w1, t1〉, . . . , 〈fn, opn, vn,wn, tn〉}With respect to the profile ψb = 〈q, b , {〈f1, v′1〉, . . . , 〈fm, v

′m〉}〉

E(πxa , ψ

b) =

∑n

i=1,j=1 E(〈fi ,opi ,vi ,wi ,ti〉,〈fj ,v′j 〉)∑ni=1 wi

0 if a mandatory criterion is not satisfied

where:

E(〈fi , opi , vi ,wi , ti〉, 〈fj , v′j 〉) =

wi if fi = fj and fi opi fj0, otherwise

(1)

12


Illustration of Policy Evaluation

Policy Evaluation

tc(identity,≥, marginal, 2, m)

tc(reputation,≥, 70, 2, o)

tc(recommendation,≥, 3, 1, o)

credential(age, alice, 25)

declaration(reputation, alice, 50)

declaration(recommendation, alice, 0)

tc(age, <, 30, 2, m)

tc(age, >, 18, 2, m)

Policy of the controller

Profile of the requester

credential(identity, alice, complete)

2 + 2 + 2 + 0 + 0

9= 0.66

13


Illustration of Policy Evaluation

Policy Evaluation




credential(age, alice, 25)

declaration(reputation, alice, 75)

declaration(recommendation, alice, 0)

tc(age, <, 30, 2, m)

tc(age, >, 18, 2, m)

Policy of the controller

Profile of the requester

credential(identity, alice, unknown)

0

13


1 Introduction

2 Research Scope


4 Adaptive TMS (A-TMS)Individual toEnvironmentIndividual ToIndividual


6 Evaluation

7 Conclusion



Environment


Partner

14


Policies Obsolescence

A

D

C

G

E

B

F

H

I

f

Frank

George

EliseCarl

Alice

Dave

Bob

Helen

?

Resources - Availability - Values- sensitivity

Trust Factors - Availability- Pertinence

Interaction outcome

Risks - Credentials falsification - Id usurpation - Reputation collusion

Policies specification context , current context15


Adaptation Meta-Policies

I Adaptive Trust Negotiation and Access Control[Ryutov et al., 2005]

I Extension of the policy language with Adaptationmeta-policies.

I When policies should be adaptedI How they can be adapted

Meta-policiesEvent : Condition ← Actions

Actions include (but not limited to) adaptation operators

I AddCriterion(π, tci)

I DelCriterion(π, fi)I UpdateCriterion(π, fi)

I RelaxCriterion(π, fi)I RestrictCriterion(π, fi)I LowerCriterion(π, fi)I HigherCriterion(π, fi)

16


Illustration of adaptation to environment

Adaptation to resource value

Instantiate(π〈_,file〉Bob ,R) :R .valuet > R .valuet−1∨

R .sensitivity t > R .sensitivity t−1 ←

RestrictCriterion(π〈_,file〉Bob , reputation),

RestrictCriterion(π〈_,file〉Bob , recommendation)

Initial Policy

π〈read,file〉bob ={〈identity,≥,marginal, 2,m〉,

〈age, >, 18, 2,m〉, 〈age, <, 30, 2, o〉,

〈reputation,≥, 50%, 3, o〉, 〈recommendation, >, 2, 1, o〉}

17


Illustration of adaptation to environment

Adaptation to resource value

Instantiate(π〈_,file〉Bob ,R) :R .valuet > R .valuet−1∨

R .sensitivity t > R .sensitivity t−1 ←

RestrictCriterion(π〈_,file〉Bob , reputation),

RestrictCriterion(π〈_,file〉Bob , recommendation)

Adapted Policy

π〈read,file〉bob ={〈identity,≥,marginal, 2,m〉,

〈age, >, 18, 2,m〉, 〈age, <, 30, 2, o〉,

〈reputation,≥, 60%, 3, o〉, 〈recommendation, >, 3, 1, o〉}

17


Adaptation of the Individual to the Partner




Environment


Partner

Social-Compliance



18


Automated Trust Negotiation

Trust Builder [Yu et al., 2003, Lee et al., 2009], IBM TE[Herzberg et al., 2000], Trust−X [Bertino et al., 2003], RT[Li et al., 2002]

I Evaluation =⇒CredentialsDisclosure

I Credentials =⇒contain sensitiveinformation

I Trust Deadlock !

subject S requests action A on resource R

Evidence for property X ?

Evidence for property Y ?

Evidence for property Y

Evidence for property X

Authorization for S to perform A on R

Controller

Requester

19


The Adaptive Trust Negotiation

I NegotiationProtocol

I NegotiationStrategy

I Utility Function

0 1 2 3 4 5

7

6

b: REQUESTa: START-

NEGOTIATIONb: ACCEP-

NEGOTIATION a: QUERY-IF

b: PROPOSE

a: ACCEPT/REJECT -PROPOSAL

b: ACCEPT/REJECT-PROPOSAL

b: CONFIRM/DISCONFIRM

a: PROPOSE

a: CONFIRM / DISCONFIRM

b: QUERY-IF

a: QUERY-IF

b: REFUSE-NEGOTIATION

a: END-NEGOTIATION

b: END-NEGOTIATION

b: END-NEGOTIATION

a: END-NEGOTIATION

20





I Utility Function

C2: confirm

C1: disconfirm

C3: propose

C3.2:accept-proposal

C3.1:refuse-proposal

query-If

C2.1 : end-negotiation

C2.1:Query-If(continue negotiation)

�1,−1�

�0, 0�

�0, 0��3, 2� �3, 3�

Controller

Requester

Controller

Controller

20





I Utility Function

uc = (O�update,r�controller +

�(xi.ν)) − (r.ς + r.ν)

ur = (O�update,r�requester +

�(xi.ς)) −

�(yi.ν)

Controller

Requester

20


Illustration

Dis-confirm

Propose

Query-If

�0, 0�Reject ProposalAccept

Proposal

Confirm

Propose

RejectProposal

Confirm End-negotiation�−3, 3�

End-negotiationaccept

�2, 0��0, 0� Accept

Proposal

End-negotiationConfirm

�−3, 3�Confirm

�0, 0�

�1, 1�

�0, 2�

r.ν = 2

r.ς = 2

O�update,r�controller = 3

O�update,r�requester = 3

c1.ν = 1

c2.ν = 2

c3.ν = 3

21


1 Introduction

2 Research Scope




Individual to CollectiveCollective to IndividualMulti-Agent BasedTMS

6 Evaluation

7 Conclusion



Environment


Partner

Social-Compliance



22


Adaptation of Individual Policies to Collective ones

23


Integration Mechanism

Integration

�f1, o1, v1, w1, t1��f2, o2, v2, w2, t2�.........................

�fn, on, vn, wn, tn�

�f1, o1, v1, w1, t1��f2, o2, v2, w2, t2�.........................


�f1, o1, v1, w1, t1��f2, o2, v2, w2, t2�.........................

�fn, on, vn, wn, tn� Ri = Rj RjRi

RjRi Ri Rj

RjRi

Ri Converge Rj Ri Diverges Rj

Ri Extends Rj Ri Restricts Rj

Ri Suffles Rj / Rj Suffles Ri

XACML [Humenn, 2003, Cover, 2007], Combination[Rao et al., 2009] and Integration [Rao et al., 2011]

24


Policies Integration Heuristics

I h1: p is at least as restrictive as the most restrictive policyI I’m sure to deny all requests both policies would have denied

I h2: p is at most as restrictive as the least restrictive policyI I’m sure to accept request that both policies would have

acceptedI h3: p is at least as restrictive as the selected policy

I I’m sure to deny all requests me/my community would havedenied

I h4: p is at most as restrictive as the selected policyI I’m sure to accept all requests me/my community would have

accepted

25


Illustration of Individual Adaptation to the Collective

Integration

(reputation,≥, 70, 2, o)(recommendation,≥, 2, 3, o)

(identity,≥, fair, 3, m)

(identity,≥, marginal, 4, m)

(identity,≥, marginal, 1, o)

(reputation,≥, 75, 4, o)

(recommendation,≥, 2, 3, o)

Collective Policy

Individual Policy

(reputation,≥, 75, 2, o)

26


1 Introduction

2 Research Scope





6 Evaluation

7 Conclusion



Environment


Partner

Social-Compliance



27


Building Collective Policies from Individual Ones

28


Combination Mechanism

Combination

�f1, o1, v1, w1, t1��f2, o2, v2, w2, t2�.........................


�f1, o1, v1, w1, t1��f2, o2, v2, w2, t2�.........................


�f1, o1, v1, w1, t1��f2, o2, v2, w2, t2�.........................


�f1, o1, v1, w1, t1��f2, o2, v2, w2, t2�.........................


Combination driven byheuristicsI h1: Selects the most

restrictive criterioneach time

I h2: Selects the leastrestrictive criterioneach time

29


Coordination for Policies Combination

1 Check if the collectivepolicy exists

2 Broadcast the call3 Policies are selected4 Policies are exchanged5 Policies are combined6 The collective policy is

generated

30




2 Broadcast the call

3 Policies are selected4 Policies are exchanged5 Policies are combined6 The collective policy is

generated

30




2 Broadcast the call3 Policies are selected

4 Policies are exchanged5 Policies are combined6 The collective policy is

generated

30




2 Broadcast the call3 Policies are selected4 Policies are exchanged

5 Policies are combined6 The collective policy is

generated

30




2 Broadcast the call3 Policies are selected4 Policies are exchanged5 Policies are combined

6 The collective policy isgenerated

30




2 Broadcast the call3 Policies are selected4 Policies are exchanged5 Policies are combined6 The collective policy is

generated

30


Illustration of Individual Policies Combination

Combination

tc(skillfulness,≥, fair, 1, o)





tc(skilfulness,≥, fair, 5, o)







tc(identity,≥, fair, 3, 0) tc(identity,≥, marginal, 5, m)

Individual Policy Individual Policy

Individual Policy

I h1: Selects the most restrictive criterion each time

31


Collective Policies Obsolescence

32


Collective Policies Evolution


Actions include (but not limited to) adaptation operators

I AddCriterion(π, tci)

I DelCriterion(π, fi)I UpdateCriterion(π, fi)

I RelaxCriterion(π, fi)I RestrictCriterion(π, fi)I LowerCriterion(π, fi)I HigherCriterion(π, fi)

33


Coordination for Collective Policies Evolution

1 Detection of PolicyObsolescence

2 Call for evolution

3 Vote for adaptation4 Compute votes5 Adapt the policy if

majority

34




2 Call for evolution3 Vote for adaptation

4 Compute votes5 Adapt the policy if

majority

34




2 Call for evolution3 Vote for adaptation4 Compute votes

5 Adapt the policy ifmajority

34




2 Call for evolution3 Vote for adaptation4 Compute votes5 Adapt the policy if

majority

34


Synthesis on Social Compliance

→ Extension of the policy language with Adaptation meta-policies


Actions includes context-awareness and social-awarenessoperators

I RelaxCriterion(π, fi)I ...

I Integrate(π1, π2, ih)

I Combine(Π′, c, ch, π′)

→ Definition of coordination protocols

35


1 Introduction

2 Research Scope





6 Evaluation

7 Conclusion




Environment


Partner

Social-Compliance



36



A

A

AA

AA

Adhesion

Association

Interactions

Community

Role

Collective Policies

A AssistantAgent

Individual Policies

ASC-TMS

Private Resource

Public Resource

T

T

T

T

T

TT

Environment

Agents

Organisation

Interaction

Control

Operation

Negotiation

DecentralizedTrust Management

CoordinationVoting/NegotiationProtocols

Norms& Organisations

37


1 Introduction

2 Research Scope

3 Trust Management System (TMS)


5 Adaptive and Socially-Compliant TMS (ASC-TMS)

6 EvaluationImplementationRepast SimulationResults

7 Conclusion38


Implementations

I Demonstrate the applicability of ASC-TMSI Deploy the model on the JaCaMo Platform

[Boissier et al., 2011]I Use of ASC-TMS in Open Innovation Community ApplicationI Extension of the model for mobiles (JaCaAndroid)

I Evaluate ASC-TMSI Implementation on Repast Simulation Platform [Collier, 2003]I Run the model on large scale populationsI Observe the benefit of ASC-TMSI Evaluate the impact of ASC-TMS on communities dynamics

39


Evaluation Objective

Study the benefit of using social compliance in trust managementwithin virtual communities

I Impact of combination on communities dynamics

I Impact of social-compliance on communities dynamics

I Correlation between social-compliance and communitiesdynamics

I Impact of evolution on communities dynamics

40


Experimental Settings

Use case: Communities for Open innovation challenges

ChallengesI Objectives: 10 000 resource unitsI Deadline: 1000 stepsI Reward: 1000 $

Rules:I Non Compliant members are ejected from their communityI Empty communities are destroyed (collapse)

Simulation MetricsI Number of communitiesI Population of each community

41


Parameters

AgentsI Policies and Credentials are randomly generatedI Collaborativeness: uniform distribution ([0,1])I Competence: normal distribution ([0,1])I Interaction: probability of 0.8

Different populations in terms of social-complianceI (With/Without) CombinationI With a probability (0/0.5/0.8/1) of IntegrationI (With/Without) Evolution

42


Impact of combination on communities dynamics

-5

0

5

10

15

20

25

0 2000 4000 6000 8000 10000 12000

Num

ber o

f Com

mun

ities

Simulation Step

No Combination - No Integration - No EvolutionCombination - No Integration - No Evolution

-5

0

5

10

15

20

25

30

35

0 2000 4000 6000 8000 10000 12000

Aver

age

Popu

latio

n Si

ze

Simulation Step

No Combination - No Integration - No EvolutionCombination - No Integration - No Evolution

43


Impact of integration on communities dynamics

-5

0

5

10

15

20

25

30

35

0 2000 4000 6000 8000 10000 12000

Num

ber o

f Com

mun

ities

Simulation Step

No Combination - No Integration - No EvolutionCombination - No Integration - No EvolutionCombination - 1.0 Integration - No Evolution

-5

0

5

10

15

20

25

30

35

40

0 2000 4000 6000 8000 10000 12000

Aver

age

Popu

latio

n Si

ze

Simulation Step

No Combination - No Integration - No EvolutionCombination - No Integration - No EvolutionCombination - 1.0 Integration - No Evolution

44


Compliance and Communities Dynamics Correlation

-5

0

5

10

15

20

25

30

35

0 2000 4000 6000 8000 10000 12000

Num

ber o

f Com

mun

ities

Simulation Step

No Combination - No Integration - No EvolutionCombination - No Integration - No EvolutionCombination - 0.5 Integration - No EvolutionCombination - 0.8 Integration - No EvolutionCombination - 1.0 Integration - No Evolution

-5

0

5

10

15

20

25

30

35

40

0 2000 4000 6000 8000 10000 12000

Aver

age

Popu

latio

n Si

ze

Simulation Step

No Combination - No Integration - No EvolutionCombination - No Integration - No EvolutionCombination - 0.5 Integration - No EvolutionCombination - 0.8 Integration - No EvolutionCombination - 1.0 Integration - No Evolution

45


Impact of Evolution on Communities Dynamics

-5

0

5

10

15

20

25

30

35

40

0 2000 4000 6000 8000 10000 12000

Num

ber o

f Com

mun

ities

Simulation Step

Combination - 1.0 Integration - No EvolutionCombination - 0.8 Integration - EvolutionCombination - 1.0 Integration - Evolution

-5

0

5

10

15

20

25

30

35

40

0 2000 4000 6000 8000 10000 12000

Aver

age

Popu

latio

n Si

ze

Simulation Step

Combination - 1.0 Integration - No EvolutionCombination - 0.8 Integration - EvolutionCombination - 1.0 Integration - Evolution

46


Results Synthesis

I Without integration (i.e. adaptation of individual trust policiesto collective ones), disappearing of communities is morefrequent

I Social compliance helps communities to work better

I Combination and evolution are important mechanisms to helpagent to maintain communities even if non social compliantmembers exist (up to 20%)

47


Contributions

I ASC-TMS is a new Hybrid Trust Approach combining Hardand Soft Trust Approaches

I ASC-TMS proposes a rich, expressive and flexible policylanguage addressing both individual and collectivedimensions

I ASC-TMS addresses both individual and collective TrustManagement and Adaptation

I ASC-TMS bridges the gap between Social Science, TrustManagement and Distributed Artificial Intelligence

48


Future Works

I Extend and enrich the evaluation of ASC-TMS (w.r.t,Populations, Heuristics, Coordination)

I Confront ASC-TMS to Social Science Theories and ExistingTrust Models

I Enrich the expressiveness of the ASC-TMS policy language

I Extend the adaptation mechanisms at the individual andcollective levels with learning capabilities to learn from pastexperiences

I Apply the adaptation mechanism to the evolution of the TrustFactors Ontology

49


Related Publications

I Yaich, R., Boissier, O., Picard, G., and Jaillon, P. (2013). Adaptiveness and social-compliance in trustmanagement within virtual communities. Web Intelligence and Agent Systems (WIAS), Special Issue: WebIntelligence and Communities (to appear).

I Yaich, R., Boissier, O., Picard, G, and Jaillon, P. (2012). An agent based trust management system for multi-agentbased virtual communities. In Demazeau, Y., Müller, J. P., Rodríguez, J. M. C., and Pérez, J. B., editors,Advances on Practical Applications of Agents and Multiagent Systems, Proc. of the 10th International Conferenceon Practical Applications of Agents and Multi-Agent Systems (PAAMS 12), volume 155 ofAdvances in SoftComputing Series, pages 217-223. Springer.

I Yaich, R., Boissier, O., Jaillon, P., and Picard, G. (2012). An adaptive and socially-compliant trust managementsystem for virtual communities. InThe 27th ACM Symposium On Applied Computing (SAC 2012), pages2022-2028. ACM Press.

I Yaich, R., Boissier, O., Picard, G., and Jaillon, P. (2011). Social-compliance in trust management within virtualcommunities. In European Workshop on Multi-agent Systems (EUMAS’11).

I Yaich, R., Boissier, O., Jaillon, P., and Picard, G. (2011). Social-compliance in trust management within virtualcommunities. In 3rd International Workshop on Web Intelligence and Communities (WI&C’11) at the InternationalConferences on Web Intelligence and Intelligent Agent Technology (WI-IAT 2011), pages 322-325. IEEEComputer Society.

I Yaich, R., Jaillon, P., Boissier, O., and Picard, G. (2011). Gestion de la confiance et intgration des exigencessociales au sein de communautés virtuelles. In 19es Journées francophones des systèmes multi-agents(JFSMA’11), pages 213-222. Cépaduès.

I Yaich, R., Jaillon, P., Picard, G., and Boissier, O. (2010). Toward an adaptive trust policy model for open anddecentralized virtual communities. InWorkshop on Trust and Reputation. Interdisciplines.

50


Thank You for Your Attention !

Questions ?

51

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X -tnl: An xml-based language for trust negotiations.

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Collier, N. (2003).

RePast : An Extensible Framework for Agent Simulation.

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References II

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Access control meets public key infrastructure, or: Assigning roles tostrangers.

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The formal semantics of XACML.

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References III

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Luhmann, N. (1990).

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References V

Rao, P., Lin, D., Bertino, E., Li, N., and Lobo, J. (2009).

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