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 attackDecoy 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 uploadsEigen-trust

Even downloading from trusted peer, still can’t guarantee for a non-polluted fileUser awarenessUser slackness

Related work

Object reputation systemCredenceThe first object-reputation system

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

Two database Vote database Correlation table

CredenceHash 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 firstThen 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

sij = sat(i, j) − unsat(i, j) Transitive trust calculated periodically

Calculate each peer’s reputation

Pi Pj Pk

Local Trust 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