Eigenrust信任模型.pptVIP

The EigenTrust Algorithm for Reputation Management in P2P Networks Sepandar D. Kamvar Mario T. Schlosser Hector Garcia-Molina Stanford University Problem Problem: Reduce inauthentic files distributed by malicious peers on a P2P network. Motivation: Problem Goal: To identify sources of inauthentic files and bias peers against downloading from them. Method: Give each peer a trust value based on its previous behavior. Some approaches Past History Friends of Friends EigenTrust Terminology Local trust value: cij. The opinion that peer i has of peer j, based on past experience. Global trust value: ti. The trust that the entire system places in peer i. Local Trust Values Each time peer i downloads an authentic file from peer j, cij increases. Each time peer i downloads an inauthentic file from peer j, cij decreases. Normalizing Local Trust Values All cij non-negative ci1 + ci2 + . . . + cin = 1 Local Trust Vector Local trust vector ci: contains all local trust values cij that peer i has of other peers j. Some approaches Past History Friends of Friends EigenTrust Past history Each peer biases its choice of downloads using its own opinion vector ci. If it has had good past experience with peer j, it will be more likely to download from that peer. Problem: Each peer has limited past experience. Knows few other peers. Friends of Friends Ask for the opinions of the people who you trust. Friends of Friends Weight their opinions by your trust in them. The Math Problem with Friends Either you know a lot of friends, in which case, you have to compute and store many values. Or, you have few friends, in which case you won’t know many peers, even after asking your friends. Dual Goal We want each peer to: Know all peers. Perform minimal computation (and storage). Knowing All Peers Ask your friends: t=CTci. Ask their friends: t=(CT)2ci. Keep asking until the cows come home: t=(CT)nci. Minimal Computation Luckily, the trust vector t, if computed in this manner