Trust Management

Trust Management

Chen DingChen YueguoCheng Weiwei

22

Outline

Introduction A computational Model

Managing Trust in a Peer-2-Peer System DMRep EigenRep

Security Concerns P2PRep XRep

Conclusion

33

Trust Management

“ a unified approach to specifying and interpreting security policies, credentials, relationships [which] allows direct authorization of security-critical actions” – Blaze, Feigenbaum & Lacy

Trust Management is the capture, evaluation and enforcement of trusting intentions.

44

Reputation: perception that an agent creates through past actions about its intentions and norms.

Trust: a subjective expectation an agent has about another's future behavior based on the history of their encounters.

Reciprocity: mutual exchange of deeds

Reputation, Trust and Reciprocity

reputation

trust reciprocity

Given social network A

Increase ai’s reputation

Increase aj’s trust of ai

Increase ai’s reciprocating actions

55

A computational Model

Defines trust as a dyadic quantity between the trustor and trustee which can be inferred from reputation data about the trustee

Two simplifications The embedded social networks are taken to be static The action space is restrict to be:

Action: α {cooperate, defect}

66

Notations for Model

Reputation: θji(c) [0,1] Let C be the set of all contexts of interest. Let θji(c) represent ai’s reputation in an embedded social

network of concern to aj for the context c C

History: Dji(c) = {E*} Dji(c) represents a history of encounters that aj has with

ai within the context c.

Trust: T (c) = E [ θ(c) | D(c)] The higher the trust level for agent ai, the higher the

expectation that ai will reciprocate agent aj’s actions.

77

A Computational Model (cont…) θab : b’s reputation in the eyes of a.

Xab(i): the ith transaction between a and b.

After n transactions. We obtained the history data History: Dab = {Xab(1), Xab(2), … , Xab(n)}

Let p be the number of cooperations by agent b toward a in the n previous encounters.

a bContext c

otherwise 0

cooperate isaction sb' if 1 (i)Xab

88

A Computational Model (cont…)

Beta distribution: p( ) = Beta(c1, c2) : estimator for θ c1 and c2: c1=c2=1 (by prior assumptions)

A simple estimator for θab

Assuming that each encounter’s cooperation probability is independent of other encounters between A and B. The likelihood for the n encounters:

L(Dab| )= p(1- )n-p

Posterior estimate for :P( |D) = Beta(c1+p, c2+n-p)

n

pab

99

A Computational Model (cont…)

Trust towards b from a is the conditional expectation of given D.

Tab = p(xab(n+1)|D) = E[ |D]

Where

ncc

pcDE

21

1

]|[

1010

Outline



Security Concerns of the communication channel P2pRep XRep

Conclusion

1111

Reputation-based trust management

2 Examples Amazon.com

•Visitors usually look for customer reviews Visitors usually look for customer reviews before deciding to buy new books. before deciding to buy new books.

eBay

•Participants at eBay’s auctions can rate each Participants at eBay’s auctions can rate each other after each transaction.other after each transaction.

Both examples use completely centralized mechanism for storing and exploring reputation data.

1212

P2P Properties

No central coordination No central database No peer has a global view of the system Global behavior emerges from local

interactions Peers are autonomous Peers and connections are unreliable

1313

Design Considerations

The system should be self-policing The shared ethics of the user population are defined and

enforced by the peers themselves and not by some central authority

The system should maintain anonymity A peer’s reputation should be associated with an opaque

identifier rather with an externally associated identity The system should not assign any profit to newcomers The system should have minimal overhead in terms of

computation, infrastructure, storage, and message complexity

The system should be robust to malicious collectives of peers who know one another and attempt to collectively subvert the system.

1414

DMRep [KZ2001]

An approach that addresses the problem of reputation-based trust management at both the data management and the semantic level

Behavioral data B: Observations t(q,p)

•a peer qa peer q P makes when he interacts with a P makes when he interacts with a peer ppeer p P.P.

B(p) = { t (p, q) or t (q, p) | q P} B

In a decentralized environment: How to access trust given B(p) and B How to obtain such B(p) and B to construct trust.

1515

DMRep

In the decentralized environment, if a peer q has to determine trustworthiness of a peer p It has no access to global knowledge B and B(p) 2 ways to obtain data:

•DirectlyDirectly by interactions by interactions

BBqq(p) = { t (q, p) | t (q, p) (p) = { t (q, p) | t (q, p) B} B}

•Indirectly Indirectly through a limited number of referrals through a limited number of referrals from witnesses r from witnesses r WWqq P P

WWqq(p) = { t (r, p) | r (p) = { t (r, p) | r W Wqq, t (r, p) B}, t (r, p) B}

1616

DMRep

Assumption: The probability of cheating within a society is

comparably low More difficult to hide malicious behavior.

Complaint c (p,q) An agent p can, in case of malicious behavior of q,

file a complaint c (p,q)

1717

A simple situation

p and q interact and later on r wants to determine the trustworthiness of p and q. Assume p is cheating, q is honest After their interaction,

•q will file a complaint about pq will file a complaint about p

•p will file a complaint about q in order to p will file a complaint about q in order to hide its misbehavior. hide its misbehavior.

If p continues to cheat, r can conclude p is the cheater by observing the other complaints about p

1818

Reputation calculation

T(p) = |{c(p,q) | q P| x |{c(q,p)| q P}| High value of T(p) indicate that p is not trustworthy Problem

•The reputation was determined based on the The reputation was determined based on the global knowledge on complains which is very global knowledge on complains which is very difficult to obtain.difficult to obtain.

1919

The storage structure

P-Grid Insert (a, k, v), where a is an arbitrary agent in the

network, k is the key value to be searched for, and v is the data value associated with the key

Query (a, k): v, where a is an arbitrary agent in the network, which returns the data values v for a corresponding query k

Properties There exists an efficient decentralized bootstrap algorithm

which creates the access structure without central control The search algorithm consists of randomly forwarding the

requests from one peer to the other. All algorithms scale gracefully. Time and space complexity

are both O(logn)

2020

Decentralized Data Management

1 6 2 3 4 5

1:301:2

Stores complaints about and

by 1

1:401:2


by 2.3

1:501:2


by 1

0:201:2


by 4,5

0:601:2


by 4,5

0:610:4


by 6

0 1

00 01 10 11Query(6,100)

Query(5,100)

Query(4,100) found!

2121

DMSRep

Access Problem: p still has to decide r’s trustworthiness

Even r is honest, it may not be reachable reliably over the network.

p q?

rq1

?

rqn

?

… rrq11rrq1n… rrqn1

rrqnn… …

? ? ? ?The exploration of the

whole network!

2222

Local computation of Trust

Assume that the peers are only malicious with a certain probability pi <= pimax <1. If there are r replicas satisfies on average pirmax < ε,

where ε is an acceptable fault-tolerance. If we receive the same data about a specific peer

from a sufficient number of replicas we need no further checks.

It also limits the depth of the exploration of trustworthiness of peers to limit the search space.

2323

Algorithm

p q

a1 a2 a3 an…

s1 s2 sw

?

a4

s3 …

Check ComplaintsW = {cri(q), cfi(q), si, fi |i=1,…w}

w: number of witness found

cri(q): number of complaints q received

cfi(q): number of complaints q filed

fi: the frequency with which si is found (non-uniformity of the P-Grid structure)

Normalized function

crcriinormnorm(q) = cr(q) = crii(q)(1-(s-f(q)(1-(s-fii/s)/s)ss), i=1,…,w), i=1,…,w

cfcfiinormnorm(q) = cf(q) = cfii(q)(1-(s-f(q)(1-(s-fii/s)/s)ss), i=1,…,w), i=1,…,w

2424

Algorithm

Function to determine trustworthyDecidep(cri

norm(q) ， cfinorm(q)) =

ifcri

norm(q)* cfinorm(q) ≤ crp

avgcfpavg

then 1 else -1

Exploring Trust. S= SUM(i=1 … w, decide(cr_i, cf_i) if S=0 Check the Trustworthy of single witness.

24

2

1

avgp

avgp cfcr

2525

DMSRep Discussion

Strength An approach that addresses the problem at both the

data management and the semantic level The method can be implemented in a fully

decentralized peer-to-peer environment and scales well for large number of participants.

Limitations environment with low cheating rates. specific data management structure. Not robust to malicious collectives of peers

2626

Outline




Conclusion

2727

How does one peer evaluate others?

Directly (by own experience) sat(i, j): +1, i downloads an authentic file from j. unsat(i, j): +1, i downloads an inauthentic file from j,

or i fails to download a file from j. local reputation value: sij=sat(i, j)- unsat(i, j).

Indirectly (by others’ experience) ask neighbors. ask friends (familiars). ask authorities (who are more reputable). ask witness.

2828

Normalizing Local Reputation Value

j ij

ijij s

sc

)0,max(

)0,max(

Local reputation vector:

Most are 0

10 ijcT

iNii ccc ),...,( 1

1..,11

1

N

jiji ceic

ijc

2929

Aggregating Local Reputation Values

Peer i asks its friends about their opinions on peer k.

Peer i asks its friends about their opinions on all peers.

Peer i asks its friends about their opinions about other peers again. (It seems like asking his friends’ friends) i

Ti cCt

2)(

j

jkijik cct

iT

i cCt

iN

ik

i

NNkNN

Nkkkk

Nk

iN

ik

i

c

c

c

ccc

ccc

ccc

t

t

t

...

...

......

......

......

......

......

...

...1

1

1

11111

11it

3030

Global Reputation Vector

Continues in this manner,

If n is large, will converge to the left principal eigenvector of C for every peer i. (C is irreducible and aperiodic)

We call this eigenvector , the global reputation vector. , an element of , quantifies how much trust the

system as a whole places peer j.

Non-distributed Algorithm

inT

i cCt

)(

it

t

t

jt

until

tt

tCt

repeat

et

kk

kTk

)()1(

)()1(

)0(

;

;

3131

Practical Issues

Pre-trust peers: P is a set of peers which are known to be trusted, is the pre-trusted vector of P, where,

Assign some trust on pre-trust peers : For new peers, who don’t know anybody else: Modified non-distributed algorithm:

othervise

PiifPpi

,0

,/1

pci

p

patCat kTk )()1( )1(

until

tt

patCat

tCt

repeat

pt

kk

kTk

kTk

)()1(

)1()1(

)()1(

)0(

)1(

;

;

3232

Distributed Algorithm

All peers in the network cooperate to compute and store the global trust vector.

Each peer stores and computes its own global trust value.

Minimize the computation, storage, and message overhead.

ik

NNik

ik

i aptctcat )...)(1( )()(11

)1(

patCat kTk )()1( )1(

3333

Distributed Algorithm (cont…)

Ai: set of peers which have downloaded files from peer i.

Bi: set of peers which peer i has downloaded files.

}

; until

return to peers allfor wait

; compute

; peers all to send

;)...)(1( compute

repeat

;for peers allquery

do{ ipeer each for

)1(

)()1(

)1(

)()(11

)1(

)0(

kjjii

ki

ki

ik

iij

ik

NNik

ik

i

jji

tcAj

tt

Bjtc

aptctcat

ptAj

i6

0

2

9

1

5

8

Predecessor: Ai

(downl oad f rom i )Successor: Bi

(downl oaded by i )

1011

7. . .

3

4

12

)(11

kitc

)(55

kitc

)(66

kitc

)(1111

kitc

)(kit

)1( kit

)1(2

kii tc

)1(7

kii tc

)1(9

kii tc

3434

Message Traffic

Mean number of acquaintance per peer : m. Mean number of iteration: k. Mean number of messages per peer: O(mk).

3535

Secure Algorithm

The trust value of one peer should be computed by more than one other peer. malicious peers report false trust values of their own. malicious peers compute false trust values for others.

Use multiple DHTs to assign mother peers. The number of mother peers for one peer is same to

all peers.

4

8

3

1

H1(1)

H2(1)

H3(1)

i

0

4

7

2

6

9

1

5

8

3

1011

12

. . .

H1(1)

H2(1)

H3(1)

3636

i

0

4

7

2

6

9

1

5

8

3

Predecessor: Ai


(downl oaded by i )Mother: Mi

(compute for i )Daughter: Di

(computed by i )

1011

12

. . .

Secure Algorithm (cont…)

Ai, Bi

0 21 9

5 12 11 #

Ai

015

11

…

…

Ai

0ic

Bi

2 0. 21 9 0. 55 12 0. 3

11

Ai

0ic

Bi

2 0. 21 9 0. 55 12 0. 3

11

3737

; Until

;return to peers allfor Wait

; Compute

; peers all to Send

;)...)(1( Compute

Repeat

;for peers allQuery

)1(

)()1(

)1(

)()(11

)1(

)0(

kjjii

ki

ki

ik

iij

ik

NNik

ik

i

jjijjii

tcAj

tt

Bjtc

aptctcat

pctcAj


i

0

4

7

2

6

9

1

5

8

3

Predecessor: Ai


(downl oaded by i )Mother: Mi

(compute for i )Daughter: Di

(computed by i )

1011

12

. . .

m Ai

0ic

Bi

2 0. 21 9 0. 55 12 0. 3

11

Ai

0ic

Bi

2 0. 21 9 0. 55 12 0. 3

11

)1(it

)2(it it...

3838


Ai

Predecessors of iBi

Successor of i

j

h

m

c

k

n

e

a

b

f

l

i

. . .

d

gi

0

4

7

2

6

9

1

5

8

3

1011

12

. . .

Mothers of Ai Mothers of BiMothers of i

H1()

H2()

H3()

H1(i

)

H1(5) H1(0)

H1(1

1) H1(1)

H1(9) H1(1

2)

H1(2)

3939

Modified Secure Algorithm

end

end

; until

;1

; compute

;for ,)( peers all to send

;)...)(1( compute

;return tofor ,)( peers allfor t wai

repeat

;for ,)( peers all to send

0;

);(

do each for

; daughters its from , ,collect

; mothers its to , , send

do ipeer each for

)()1(

)1(

)()(11

)1(

)(

)(

kk

tt

BjjHashtc

aptctcat

tcAjjHash

BjjHashpctc

k

dHashi

Dd

DcBA

McBA

ki

ki

itk

ddj

dk

NNdk

dk

d

kjjddt

itddjk

ddj

t

i

iddd

iiii

4040

Message Traffic

Mean number of acquaintance per peer: m. Mean number of iteration: k. Number of mothers for one peer: t. Mean number of message per peer: O(tmk).

4141

Using Global Reputation Values

Isolate malicious peers. download from reputable peers.

Incent peers to share file. reward reputation.

Allow the newcomers to build trust. provide a probability of 10% to be selected. reward new comers greatly.

Balance the load. download probabilistically based on trust values. set up maximum reputation (e.g. sij<MAX Value).

Reputati on Reputati on

Noraml Popul arMal i ci ous

4242

Limitation of EigenRep

Cannot distinguish between newcomers and malicious peers.

Malicious peers can still cheat cooperatively A peer should not report its predecessors by itself.

Flexibility How to calculate reputation values when peers join

and leave, on line and off line.

When to update global reputation values? According to the new local reputation vector of all

peers.

Anonymous? A mother peer know its daughters.

4343

Outline

Introduction A computational Model Trust management in P2P system


Security Concerns P2pRep XRep

Conclusion

4444

P2PRep & XRep

Not focus on computation of reputations Security of exchanged messages

Queries Votes

How to prevent different security attacks

4545

Using Gnutella for reference A fully P2P decentralized infrastructure Peers have low accountability and trust Security threats to Gnutella

•Distribution of tampered informationDistribution of tampered information

•Man in the middle attackMan in the middle attack

P2PRep & XRep

4646

P select a peer among those who respond to P’s query

P polls its peers for opinions about the selected peer

Peers respond to the polling with votes P uses the votes to make its decision

Sketch of P2PRep

4747

Sketch of P2PRep Cont’d

To ensure authenticity of offerers & voters, and confidentiality of votes

Use public-key encryption to provide integrity and confidentiality of messages

Require peer_id to be a digest of a public key, for which the peer knows the private key

4848

P2PRep

Two approaches: Basic polling

•Voters do not provide Voters do not provide peer_id peer_id in votesin votes Enhanced polling

•Voters declare their Voters declare their peer_id peer_id in votesin votes

4949

P2PRep – Basic Polling (a)

Peers SSInitiator P

QueryHit(IP,port,speed,Result,peer_id)

Query(search_string)

Select top list T of offerers

Generate key pair (PKpoll, SKpoll)

TrueVote( Votesj )

PollReply( {(IP,port,Votes)}PKpoll )

Remove suspicious votes

Select random subset V’

Poll(T, PKpoll)

TrueVoteReply(resonse)

If response is negative, discard Votes j

Select peer s for downloading

P *

P *

Si P, (Si S)S)

Vi P, (Vi V)V)

P Vj, (Vj V’)V’)D

Vj P, (Vj V’)V’)D

5050

P2PRep – Basic Polling (b)

Peer sInitiator P

Response([r]SKs, PKs)

Challenge(r)

If h(PKs)=peer_ids &&{[r]SKs}PKs=r: download

Update experience_repository

Generate random string r

P sD

s PD

5151

P2PRep

Two approaches: Basic polling

•Voters do not provide Voters do not provide peer_id peer_id in votesin votes Enhanced polling

•Voters declare their Voters declare their peer_id peer_id in votesin votes

5252

P2PRep – Enhanced Polling (a)

Peers SSInitiator P

QueryHit(IP,port,speed,Result,peer_id)

Query(search_string)

Select top list T of offerers

Generate pairs (PKpoll, SKpoll)

AreYou( peer_idj )

PollReply( {[(IP,port,Votes,peer_idi)]SKi, PKi}PKpoll )

Remove suspicious votes

Select random subset V’

Poll(T, PKpoll)

AreYouReply(resonse)

If response is negative, discard Votes j

Select servent s for downloading

P *

P *

Si P, (Si S)S)

Vi P, (Vi V)V)

P Vj, (Vj V’)V’)D

Vj P, (Vj V’)V’)D

5353

P2PRep – Enhanced Polling (b)

Peer sInitiator P

Response([r]SKs, PKs)

Challenge(r)

If h(PKs)=peer_ids &&{[r]SKs}PKs=r: download

Update experience_repository

Generate random string r

P sD

s PD

5454

Comparison: Basic vs Enhanced

Basic polling all votes are considered equal

Enhanced polling peer_ids allow p to weight the votes based on v’s

trustworthiness

5555

Discussion

In enhanced polling, voters also provide IP & port in PollReply message

Discussion: IP & port, and AreYou message can be omitted Explanation 1:

•basic polling needs basic polling needs IP & portIP & port to check truthfulness of to check truthfulness of VotesVotes

•voter’s private key guarantees this in enhanced pollingvoter’s private key guarantees this in enhanced polling Explanation 2:

•the paper explains that AreYou message checks the the paper explains that AreYou message checks the truthfulness of (IP,Port)truthfulness of (IP,Port)

•the the offerer’sofferer’s (IP,Port) needs to be checked as later we (IP,Port) needs to be checked as later we need download from it. For voter, we only need the need download from it. For voter, we only need the truthfulness of Votestruthfulness of Votes

5656

P2PRep: Security Improvements (1)

Distribution of Tampered Information B responds to A with a fake resource

P2PRep Solution: A discovers the harmful content from B A updates B’s reputation, preventing further

interaction with B A become witness against B in pollings by others

5757


Man in the Middle Attack Data from C to A can be modified by B, who is in the

path

•A broadcasts a Query and C respondsA broadcasts a Query and C responds

•B intercepts the QueryHit from C and B intercepts the QueryHit from C and rewrites it with B’s IP & portrewrites it with B’s IP & port

•A receives B’s replyA receives B’s reply

•A chooses B for downloadingA chooses B for downloading

•B downloads original content from C, B downloads original content from C, modifies it and passes it to Amodifies it and passes it to A

5858


Man in the Middle Attack P2PRep addresses this problem by including a challenge-

response phase before downloading To impersonate C, B needs

•C’s private keyC’s private key

•To design a public key whose digest is C’s To design a public key whose digest is C’s identifieridentifier

Public key encryption strongly enhances the integrity of the exchanged messages

Both versions address this problem

5959

XRep

Extended from P2PRep Combining servent-based & resource-based

reputations Servent-based Reputation

•Associated with Peer IdentifierAssociated with Peer Identifier Resource-based Reputation

•Coupled to resource’s contentCoupled to resource’s content

6060

Two Requirements Peer_id is a digest of its public key Resource_id is a digest of its content

Each peer maintains two experience repositories Servent Repository Resource Repository

XRep

6161

XRep protocol consists of 5 stages: Recource searching Rescource selection & Vote Polling Vote evaluation Best peer check Resource downloading

Sketch of XRep

6262

XRep

Differences from P2PRep QueryHit contains resource digests in ResultSet Vote Polling: ask peers to vote on resource or on

the peers who offer the resource Vote Reply: each peer can respond with votes on

resources or peers

Similar to P2PRep, public key encryption is used

6363

XRep: Security Consideration

Distribution of Tampered Information Man in the middle attack

6464

XRep: Improvements (1)

Decoupling of resource from offerers permits parallel downloads P can ask different offerers for different resource

fragments

6565

XRep: Improvements (2)

Combining servent-based & resource-based reputations Both have shortcomings and advantages

Servent-based Resource-based

Reputation’s life cycle

shorter due to peer_id changes

good resource always recognizable

Cold start avoid cold start for new resource

avoid cold start for new peers

Performance bottleneck

may direct all downloads to most reputable peers

avoids bottleneck for most reputable peers

6666

Outline

Introduction A computational Model Trust management in P2P system


Security Concerns P2pRep XRep

Conclusion

6767

Conclusion

Reputation-based Trust Management Reputation Computation & Management

DMRep EigenRep


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Trust Management

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