Systematic clustering method for l-diversity model

Nowadays privacy becomes a major concern and many research efforts have been dedicated to the development of privacy protecting technology. Anonymization techniques provide an efficient approach to protect data privacy. We recently proposed a systematic clustering method based on kanonymization technique that minimizes the information loss and at the same time assures data quality. In this paper, we extended our previous work on the systematic clustering method to l-diversity model that assumes that every group of indistinguishable records contains at least l distinct sensitive attributes values. The proposed technique adopts to group similar data together with l-diverse sensitive values and then anonymizes each group individually. The structure of systematic clustering problem for l-diversity model is defined, investigated through paradigm and is implemented in two steps, namely clustering step for kanonymization and l-diverse step. Finally, two algorithms of the proposed problem in two steps are developed and shown that the time complexity is in O( 2 k ) in the first step, where n is the total number of records containing individuals concerning their privacy and k is the anonymity parameter for k-anonymization.