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A Novel Reputation Management Mechanism with Forgiveness in P2P File Sharing Networks
Author(s) -
Mingchu Li,
Junlong Wang,
Kun Lu,
Cheng Guo,
Xing Tan
Publication year - 2016
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2016.08.055
Subject(s) - reputation , computer science , forgiveness , reputation system , upload , file sharing , computer security , reciprocity (cultural anthropology) , reputation management , peer to peer , compensation (psychology) , internet privacy , world wide web , psychology , social psychology , law , the internet , political science
In peer-to-peer (P2P) file sharing networks, it is common practice to manage each peer using reputation systems. A reputation system systematically tracks the reputation of each peer and punishes peers for malicious behaviors (like uploading bad file, or virus, etc). However, current reputation systems could hurt the normal peers, since they might occasionally make mistakes. Therefore, in this paper, we introduce forgiveness mechanism into the EigenTrust reputation system to reduce such malicious treatments and give them opportunities to gain reputation back. Particularly, we take four motivations (the severity of current offence, the frequency of offences, the compensation and the reciprocity of the offender) into consideration to measure forgiveness. The simulation work shows that the forgiveness model can repair the direct trust breakdown caused by unintentional mistakes and lead to less invalid downloads, which improves the performance of P2P file sharing systems

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