Graph Based Local Recoding for Data Anonymization

Abstract

Releasing person specific data could potentially reveal the sensitive information of an individual. kanonymity is an approach for protecting the individual privacy where the data is formed into set of equivalence classes in which each class share the same values. Among several methods, local recoding based generalization is an effective method to accomplish k-anonymization. In this paper, we proposed a minimum spanning tree partitioning based approach to achieve local recoding. We achieve it in two phases. During the first phase, MST is constructed using concept hierarchical and the distances among data points are considered as the weights of MST and in the next phase we generate the equivalence classes adhering to the anonymity requirement. Experiments show that our proposed local recoding framework produces better quality in published tables than existing Mondrian global recoding and k-member clustering approaches.