Proposal pollution prevention in the p2p file sharing system
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Proposal Pollution prevention in the P2P file sharing system. Presenter: Elaine. Motivation. P2P traffic has dominated 60% traffic in the internet, P2P file-sharing is an important application. Recently, many existing works have shown that network is rife with deliberate polluted files

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Proposal Pollution prevention in the P2P file sharing system

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Proposal pollution prevention in the p2p file sharing system

ProposalPollution prevention in the P2P file sharing system

Presenter: Elaine


Motivation

Motivation

  • P2P traffic has dominated 60% traffic in the internet, P2P file-sharing is an important application.

  • Recently, many existing works have shown that network is rife with deliberate polluted files

    • Definition of polluted file

      • File content does not match its file description


Motivation1

Motivation

  • Application environment description

    • A P2P file-sharing application with search capability

    • File-sharing apps use meta-data for searching

    • Content Hash

    • Response result list

      • For a given file A

  • Version # of copies

  • H1 40(P2,P7….P80,P91,P102)

  • H2 23(P3,P5….P33,P54..)

  • : :

  • : :

  • Hn 2(P10,P17)


Related work

Related work

  • Different types of pollution attack

    • Decoy injection: Meta data is the same, H is different

      • File content is damaged or not match

    • Hash corruption: H is the same, but content is polluted

      • Two different files could be maliciously hashed to the same hashed ID, dangerous especially when parallel downloading


Related work1

Related work

  • Peer-reputation systems exist.

    • Based on the peer’s history of uploads

    • Eigen-trust

  • Even downloading from trusted peer, still can’t guarantee for a non-polluted file

    • User awareness

    • User slackness


Related work2

Related work

  • Object reputation system

    • Credence

    • The first object-reputation system

      • Voting after each object downloading Issuing a vote-gather query  Evaluating the object reputation.

    • Two database

      • Vote database

      • Correlation table


Related work3

Related work

  • Credence

    • Hash corruption Mechanism still can not be avoid because it didn’t verify for the source.

    • Disadvantages

      • Votes database could be costly

      • The correlation is not accurate if two peers didn’t download enough common objects.


Problem definition

Problem Definition

  • The best way to prevent the spreading of pollution is to

    • Select a non-polluted file first

    • Then select the trust peers to download

  • Version # of copies

  • H1 40(P2,P7….P80,P91,P 102)

  • H2 23(P3,P5….P33,P54..)

  • : :

  • : :

  • Hn 2(P10,P17)


Proposal pollution prevention in the p2p file sharing system

Idea

  • Designing a robust pollution-prevention system

  • Mechanism operations

    • Vote after downloading each object

    • Calculate each peer’s reputation periodically

    • Searching for object and collecting votes

    • Calculate object’s reputation before downloading and select peers to download from.


Calculate each peer s reputation

Local Trust

Local Trust

Pi

Pj

Pk

Transitive Trust

Calculate each peer’s reputation

  • Each time peer i download a file from peer j, it may rate the transaction as positive or negative value

  • sij = sat(i, j) − unsat(i, j)

  • Transitive trust calculated periodically


Searching for object and collecting votes

Searching for object and collecting votes

Query for vote

Query for object


Select object and trusted peers to download

Select object and trusted peers to download

  • Weigh collecting votes by the trust value to the voter

  • Select a non-pollution version

  • Select a group of trusted peers to download from


Experimental plan

Experimental plan

  • Compare with existing strategy of

    • Peer reputation system

    • Object reputation system (Credence)

    • random, redundant best, redundant random downloading

  • Metrics

    • From user perspective

      • The necessary time for downloading a clean file

    • From network perspective

      • The amount of traffic generated by the transmission of polluted files

      • The pollution level varies with time, and the pollution level at the steady state

        • Pollution level: The ratio of good copies and bad copies in the network

  • Human factor

    • User awareness

    • User slackness

    • Willingness to vote


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