Implementation Details of Anonymization of Sensitive Labels in Collaborative Data Publishing

Nowadays, no: of research and development in privacy preserving publishing of social networking data. The privacy is one of the important concerns for the social network. So, our aim is protect the privacy preserving publishing of social networking data in collaborative environment using the k-anonymity model, the set of nodes share the same attributes so, the sensitive information may gain access by the intruder. So, our aim is that how to protect the sensitive attributes of individuals and the structural information of social network data in collaborative environment. In this paper we study the existing structure anonymization mechanisms and defined a model called k-degree-l-diversity anonymity model, which takes into consideration the structural information and sensitive labels of individuals in collaborative data publishing. Collaborative data publishing means that different publishers publish their data independently for e. g. in hospitals, wish to publish anonymzed view of their data. We have large no: of dataset its not anonymized so; the attacker should be hack the data. So, they protect the datas of dierent publishers from the hackers by using a new anonymization method called k-degree l diversity model.