As a concept stemmed from social field, we argued that, in P2P networks, peers' recommendation behaviors and functional behaviors should be explicitly separated, thus we propose the HopRec scheme which uses hop-based recommendation ability to improve the accuracy of reputation ranking in P2P networks. Our contributions lie in the following aspects: firstly, we adopt the simple but effective idea to infer peer's recommendation ability (RA): the farer away that peer is from the initial malicious seeds, the higher RA that peer should have; Then, the computation of reputation rankings appropriately reflects peer's different RA. The simulation results show that, in comparison with Eigentrust-like algorithms, HopRec can be robust to sybils and front peers attacks, and achieve significant performance improvement. Moreover, we compare HopRec with two related schemes, Poisonedwater and CredibleRank, and found that: in hospitable P2P environment, HopRec can obtain better performance than Poisonedwater, and can achieve the comparable performance as CredibleRank, with less computation overhead then CredibleRank. Finally, we also show that, if the initial good and malicious seeds could be selected based on peers' degrees, then HopRec and CredibleRank can achieve perfect performance.
展开▼
