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Proposal Pollution prevention in the P2P file sharing system

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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  1. ProposalPollution prevention in the P2P file sharing system Presenter: Elaine

  2. 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

  3. 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)

  4. 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

  5. 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

  6. 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

  7. 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.

  8. 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)

  9. 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.

  10. 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

  11. Searching for object and collecting votes Query for vote Query for object

  12. 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

  13. 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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