Privacy-Preservation of Centralized and Distributed Social Network by Using L-Diversity Algorithm

Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called k-anonymity has gained popularity. In a k-anonymized dataset, each record is indistinguishable from at least k−1 other records with respect to certain “identifying” attributes. The problem of Social Network is getting secured data from unauthorized access of database. To consider the distributed configurations in which the network data is split between several data holders. The data is divided between a numbers of data holders. The plan is to get there at an anonymized view of the combined network without informative to any of the data holders. Two variants of an anonymization algorithm which is based in order clustering. High sensitive data has been secured in l-diversity algorithms. Based on the retrieval of data from the database, calculation of data loss has to be done. Also the analyzing of data that how secure the database and also by calculating the data loss. In addition to building a formal foundation for l-diversity, we show in an experimental evaluation that l-diversity is practical and can be implemented efficiently.