A Simulation Study of P2P File Pollution Prevention Mechanisms

Chia-Li Huang, Polly HuangNetwork & Systems Laboratory

Department of Electrical EngineeringNational Taiwan University

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Outline

• Background • Problem• Methodology• Simulation Environment & Results• Conclusion

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• P2P file sharing system with search capability• Issue a query with keywords to search for a file

Meta-Data(For keyword

matching)

Content

A file in system

songA

Song title, length, encoding scheme of songA

Overview of P2P file sharing system

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HashValue

Hash function

Different versions of songA

Mp3, wma,…

How a user searches for a file

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P2P network

Query for songA

Peer1

Responses for songA

Randomly choose a source for download

Pollution in file sharing system

• Definition of a polluted file– Meta-data description doesn’t match its content!

• Current P2P networks are full of polluted files [1]– Unintentional – Intentional

Meta-Data A

Content B

[1] J. Liang, Y. X. R. Kumar, and K. Ross, “Pollution in p2p file sharing systems,” in Proceedings of IEEE Infocom, 20055

Problem

• Pollution in P2P system results in the following problems– Reduce content availability– Increase redundant traffic

• There are different anti-pollution mechanisms existing– Which one is better?

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Methodology• Simulation study on anti-pollution mechanisms– Extending a P2P simulator [2]– Existing anti-pollution mechanisms

• Peer reputation system– Choose a reputable peer to download file– EigenTrust [3]

• Object reputation system– Choose a reputable version of a file to download– Credence [4]

– Different pollution attacks– User behavior

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[2] M. Schlosser and S. Kamvar, “Simulating a file-sharing p2p network ,” In Proc.of SemPGRID 2003[3] S. D. Kamvar, M. T. Schlosser, and H. Garcia-Molina, “The eigentrust algorithm for reputation management in p2p networks”, in Proceedings of the Twelfth International World Wide Web Conference,[4] K. Walsh and E. G. Sirer, “Experience with an object reputation system for peer-topeer filesharing”, in Proceedings of Networked System Design and Implementation (NSDI), May 2006.

• Rate a peer by it’s uploading history from the whole system

Peer Reputation System : EigenTrust

Peeri Peerj

Cij=

Local reputation (Cij) Global reputation(Ti)

Good file

• Rate a peer by it’s uploading history from the whole system

• Choose a reputable peer to download

T1 =?

T2

Peer2

Peer1

Peer3

C21 C31

T3

Peer Reputation System : EigenTrust

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Peeri Peerj

Cij=

Local reputation (Cij) Global reputation(Ti)

Bad file

1.0

0

9.0

0

C 12

C 14

0

0

Peer 2

Peer 4

Peer 1

A peer will store a list of local reputations

T1 = C21* T2 + C31*T3

• Calculate an object (file) reputation by weighted votes– After download vote it as clean or polluted

Query of song A

Vote-gather Query of song A

Object Reputation System : Credence

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Vote database of Peer1

Obj3 Good

Obj4 Good

Obj5 Bad

Obj6 Bad

P2

P1

P3

P4

P5

• Calculate an object (file) reputation by weighted votes– After download vote it as clean or polluted

• Choose a reputable version for download

Votep3

Version1

Responses of song A

Vote-responses of song A Version no.

Sources Received Votes

VersionReputation

Version 1 P2 , P3 VoteP2 VoteP3

CorrP1,P2*VoteP2 + CorrP1,P3*VoteP3

Version 2 P4 VoteP4

VoteP5

CorrP1,P4*VoteP4 + CorrP1,P5*VoteP5

Object Reputation System : Credence

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Vote database of Peer2

Obj1 Bad

Obj2 Bad

Obj3 Good

Obj4 Good

Vote database of Peer3

Obj5 Good

Obj6 Good

Obj7 Bad

Obj8 Bad

Vote database of Peer1

Obj3 Good

Obj4 Good

Obj5 Bad

Obj6 Bad

Received Responses of P1

Positive correlation

Negativecorrelation

Version 2

P2

P1

P3

P4

P5

Version1

Votep2

Vote p4

Vote p5

random choose a source

Pollution Attacks• Prevalent pollution attacks [5]

– Decoy Insertion– Hash Corruption

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A clean file ofSongA

Hash Corruption

MA

H1

Clean

MA

H1

Corrupted

MA

H2

Corrupted

[5] F. Benevenuto, C. Costa, M. Vasconcelos, V. Almeida, J. Almeida, and M. Mowbray,“Impact of peer incentives on the dissemination of polluted content”, in SAC ’06

Decoy Insertion

[6] U. Lee, M. Choi, J. Cho, M. Y. Sanadidi, and M. Gerla, “Understanding pollution dynamics in p2p file sharing”, in Proceedings of the 5th International Workshop on Peer-to-Peer Systems (IPTPS’06), 2006

• Slackness [6]– A period of time between download completion and quality check– Bimodal distribution

• Awareness [6]– The probability that a user can correctly recognize

a file being polluted – No clear characteristic is observed

• high-awareness prob. = 0.8 • low-awareness prob. = 0.2

User Behavior

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Outline

• Background • Problem• Methodology• Simulation Environment & Results• Conclusion

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Simulator Description• P2P Query Cycle based simulator – In a cycle, each peer issues one query and repeats

downloading until satisfied

• Extension– Types of attacks

• Decoy Insertion, Hash Corruption– Anti-Pollution mechanisms

• EigenTrust, Credence– User behavior

• Slackness, awareness

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Simulation Scenario

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Type of Peer

MaliciousAlways share polluted files based on different attack s

Normal Share what they’ve downloaded

Simulation Setup

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Peers [9]# of normal peers# of malicious peers# of neighbors

100 100 6

ContentDistribution

[8] [9]

# of Categories in the system# of Categories of each peerFiles in a category andVersions of each fileFile size distribution

20At least 4Zipf distribution with α = 1

Table 1

Simulation # of cycles# of experiments

30010

[8] S. D. Kamvar, M. T. Schlosser, and H. Garcia-Molina, “The eigentrust algorithm for reputation management in p2p networks”, in Proceedings of the Twelfth International World Wide Web Conference,[9] K. Walsh and E. G. Sirer, “Experience with an object reputation system for peer-topeer filesharing”, in Proceedings of

Networked System Design and Implementation (NSDI), May 2006.[10] N. Leibowitz, M. Ripeanu, and A. Wierzbicki, “Deconstructing the Kazaa network”, Internet Applications. WIAPP 2003.

Proceedings. The Third IEEE Workshop

Size 1KB 10KB 100KB 1MB 10MB 100MB 1GB

Percentage 1.5% 1.83% 26.67% 10.00% 35.00% 15.00% 10.00%

Table 1. File size distribution of P2P traffic [10]

Critical Evaluation Parameters

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AttackFraction of high-aware

peers in the network

slackness

Decoy-Insertion80%50%20%

Yes or No

Hash-corruption80%50%20%

Yes or No

Decoy-Insertion & Hash- corruption

80%50%20%

Yes or No

Evaluate different anti-pollution mechanisms

under the following scenarios

• Successful Downloading Rate (per cycle)

• Redundant Traffic (per cycle)

• Reduced traffic Ratio(compared to randomly selection )

R

MR

M RT

RTRTRTR j

j

Evaluation metrics

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n

i i

n

iM

tSDR

j

1

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Symbol Descriptions

Mj Mechanism of Credence or EigentTrust

n # of high-aware peers

ti Trials of downloads for a peer i to geta clean file in a cycle

PT Polluted traffic

CT Control traffic

jjj MMM CTPTRT

Total successful downloads

Total trials of downloads

Redundant traffic generated by random selection

Reduced redundant traffic by using Mj

Simulation Result

• Compare the performance of different anti-pollution mechanisms under different scenarios– EigenTrust– Credence– Random

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Successful Downloading Rate

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Credence is more sensitive to the type of attacks

Under Hash-Corruption attackUnder Decoy-Insertion attack

Credence identifies a clean version before download

EigenTrsut rates on peers, not the hashvalue

Converge after 100 cycles

Credence > EigenTrust

EigenTrust > Credence

Observation 1 : User awareness

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EigenTrustCredence

Reasons:1. Fewer peers share clean files

2. Less peers correctly operate the reputation system

Observation 1 : User awareness

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EigenTrustCredence

User awareness is critical on anti-pollution mechanisms

Reasons:1. Fewer peers share clean files

2. Less peers correctly operate the reputation system

Observation 2 : User slackness

User slackness has negative effect onAnti-pollution mechanisms

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Pollution held by a user longer has more chances to be download

Discussion

• User behavior has significant effect on anti-pollution mechanisms

• Credence performs better under Decoy Insertion, while Eigentrust performs better under Hash Corruption– Type of attacks can’t be predicted– Suggest a hybrid anti-pollution mechanism

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Versions Sources

Version1 P1, P5, P7, . . .P124

Version2 P14, P21, P35

: :

VersionN P4, P2

Hybrid Anti-pollution Mechanism

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Response -list

Step1:Select a reputable version byobject reputation mechanism

Step2:Select a reputable peer by peer reputation mechanism

P2P network

Query for songA

Successful Downloading Rate

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Decoy Insertion Hash Corruption

Ensure both a reputable version and a source confront different types of attacks

Successful Downloading Rate

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Decoy Insertion Hash Corruption

Ensure both a reputable version and a source confront different types of attacks

Hybrid mechanism performs the best under both attacks

Reduced-Traffic Ratio

• Hybrid mechanism generate more control traffic– Trade-off between pollution traffic & control traffic

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The trade-off is worthwhile

Decoy Insertion Hash Corruption

Conclusion

• Both peer reputation and object reputation system are necessary

• User behavior has significant influence on anti-pollution mechanisms

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Thank you!

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