An Ensemble Classification Technique to Control Unwanted Messages in Online Social Networking

Online Social networks are vulnerable to the private data leakage through many data mining algorithm through Relational mining and Classification Algorithm. We explored many problems in preserving the private data of the social networks users and they currently do not provide any mechanism to enforce privacy concerns over data associated with multiple users. To this end, we propose a approach for protection of shared data associated with multiple users in OSNs through the devised sanitation techniques. We formulate an sanitation technique in terms of access control model to capture the essence of multiparty authorization requirements, along with a multiparty policy specification scheme and a policy enforcement mechanism. In additional, we present a logical representation of our access control model which allows us to leverage the features of existing logic solvers to perform various analysis tasks on our model. . Comparison with state-of-the-art data stream classification techniques establishes the effectiveness of the proposed approach