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 Definition of polluted file  File content does not match its file description

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 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 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 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

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. Related work

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)

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.

Each time peer i download a file from peer j, it may rate the transaction as positive or negative value s ij = sat(i, j) − unsat(i, j) Transitive trust calculated periodically Calculate each peer’s reputation PiPi PjPj PkPk Local Trust Transitive Trust

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

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 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