Abstracto

Privacy Preservation for User Profiles In Social Networks

Ms. U.P. Umasree, Mr. V. Bhaskar, ME,

A social network describes entities and connections between them. The entities are often individuals; they are connected by personal relationships, interactions, or flows of information. Social network analysis is concerned with uncovering patterns in the connections between entities. It has been widely applied to organizational networks to classify the influence or popularity of individuals and to detect collusion and fraud. Social network analysis can also be applied to study disease transmission in communities, the functioning of computer networks, and emergent behavior of physical and biological systems. Class learning algorithms are used on released data to predict private information. Inference attacks are initiated using released social networking data to predict private information. Collective inferences are used to discover sensitive attributes of the data set. Social network data classification is carried out with the combination of node details and connecting links in the social graph. Navie bayes classification algorithm is tuned to classify friendship links in a network. Local classifier, a relational classifier, and a collective inference algorithm are the three components used in the social network analysis. Local classifiers are a type of learning method that are applied in the initial step of collective inference. The relational classifier analyzes the link structure and labels of the node to identify a model for classification. Collective inference algorithm is used to increase the classification accuracy from the local and relational data values. The privacy preservation model is designed to protect sensitive attribute in user profiles from social networks. The user can select attribute for hiding process. Local classification and relational classification are applied to estimate anonymity levels. The system manages the key node alter and remove operation.