A decentralized and personalized spam filter based on social computing

Spam is an imperative problem to the email communication today. Different users may have different views on judging spam which makes it difficult to filter spam from normal emails for email server. We found users with similar interest may have similar opinions. So in this paper, we proposed a spam filtering approach in which a collaborative and personalized spam filter based on social network is developed. The key idea is to enable users to push spam reports to their social network friends with similar interest, which reflects collaboration and personalization. Our proposal takes advantage of push technology to share user's individual spam knowledge with others via social network, which utilizes wisdom of crowds to resist spam. According to interest similarity among users, a user can determine whether to push spam reports to his friends with the purpose of taking user's individual interest into consideration. We integrate an interest-based spam filter with a basic Bayesian filter to discriminate spam from legitimate emails. The evaluation of our proposal shows that it significantly improves the performance compared with Bayesian filter according to the accuracy rate.