Social Networks Privacy-Preserving On Collaborative Tagging and Spam Filter Using Naive Bayes Algorithm

Collaborative tagging is one of the most popular services available in social networks, and it allows user to classify either online or offline resources based on their feedback, deliver in the form of tags. Although tags may not be secret information the wide use of collaborative tagging services increases the risk, thereby seriously compromising user privacy. In this paper, we make a contribution towards the development of a privactivey-preserving collaborative tagging service, by showing how a specific privacy-enhancing technology, namely tag suppression, can be used to protect end-user privacy.In most group key management protocols, group members are authenticated by the group leader “one by one.” That is, n authentication messages are required to authenticate n group members. Then, these members share one common group key for the group communication. In our batch authentication protocols, users are simultaneously authenticated by the requester that is, one authentication message is required to authenticate n session peers.Spam is commonly defined as irrelevant comments or text, the goal of spam is to distinguish between irrelevant and relevant comments. Naive Bayes classifiers are among the most successful known algorithms for learning to classify text documents.Bayesian spam filtering has become a popular mechanism to distinguish illegitimate spam texts from legitimate texts