A Novel Approach to Prevent Personal Data on a Social Network Using Graph Theory

Online social network such as Twitter, LinkedIn are widely used now a days. In these people lists their personal information and favorite activities which meant to be secure. Private information leakage becomes key issues with social network users. Apart from this user are hampered with various types of malicious data attacks which feel users very embarrassing in a real life. Also manual filtering for such a large data is not feasible at all. So various users stay away from social network sites to avoid such activities the social network architecture should be improved so that normal user can take a relief. Proposed work is an automatic prevention mechanism for such a heavy data using NLP and data mining approach. Objective of work is creation of real time rule sets to filter data using graph theory.