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Research Article Open Access
Privacy becoming a major concern in publishing sensitive information of the individuals in the social network data. A lot of anonymization techniques are evolved to protect sensitive information of individuals. kanonymity is one of the data anonymization framework for protecting privacy that emphasizes the lemma, every tuple should be different from at least k-1 other tuples in accordance with their quasi-identifiers(QIDs). Researchers have developed privacy models similar to k-anonymity but still label-node relationship is not well protected. In this paper, we propose a novel synergized k-degree l-diversity t-closeness model to effectively anonymize graphical data at marginal information loss, thereby controlling the distribution of sensitive information in graphical structure based on the threshold value. Experimental evidences indicate the substantial reduction in information loss ratio during synergy.