Tagging systems are known to be particularly vulnerable to tag spam. Due to the self-organization and self-maintenance nature of Peer-to-Peer (P2P) overlay networks, users in the P2P tagging systems are more vulnerable to tag spam than the centralized ones. This paper proposes SpamResist, a novel social reliability-based mechanism. For each tag search, Spam-Resist client groups the search respondents into two categories, namely unfamiliar peers and interacted peers according to the fact whether the client has interacted with such respondents. For the two different categories of peers, the client computes their reliability degrees, and then utilizes these reliability degrees as weights to rank search results. To obtain higher quality search results, we propose a socially-enhanced mechanism, considering social friends can share their previous experience and help improve both the performance and convergence of SpamResist. Finally, the experimental results illustrate that SpamResist can effectively defend against tag spam and work better than the existing search models in P2P tagging systems.
['Ennan Zhai', 'Ruichuan Chen', 'Eng Keong Lua', 'Long Zhang', 'Huiping Sun', 'Zhuhua Cai', 'Sihan Qing', 'Zhong Chen']
SpamResist: Making Peer-to-Peer Tagging Systems Robust to Spam