Neural Network based Privacy Preservation Data Mining for Social Network Sites

Abstract

Due to increasing use of Online Social Networks (OSNs) applications such as Facebook, Twitter, etc., several research challenges related to security and privacy of OSN users introduced. The end users of OSNs expecting that social networks should strong enough to preserve their private data secure from the attackers. In this research work, we introduce the novel data mining based framework to protect the OSNs from various privacy violation concerns. Risk of disclosure of individual’s confidential information have risen tremendously due to widening of social network and publication of its data. From security perspective privacy retaining becomes mandatory prior to service providers publish network information. Recently, preservation of privacy in data of social networks has become most challenging and concerning problem as it has caught our lives in a dramatic way. Various methods of anonymization exist  that helps in retaining privacy of social networking. By developing graph and nodes degree,  k-Anonymity and among all available techniques is  an utmost one that assist in delivering security of information on internet. With major manipulation in editing of node techniques in this research paper, improvement of K-anonymity has been explained. With  integration of same degree in one group, clusters are developed and processes are repeated untill recognition and identification of noisy data is done. For minimizing node misplacement in groups an Advanced Cuckoo Search is commenced and processed. To cross verify structure and for reducing node miss placement in groups outcome of  Cuckoo Searches are combined with Feed Forward Back Propagation Neural Networks. We have computed the Information Loss and Average Path Length of proposed model. These results showed that the reduction in these parameters to a good extent compared to other implementations. These values of Information Loss and Average Path Length in case of a network with 9 nodes are obtained as 0.24 and 32.2 respectively.