Systematic clustering method for l -diversity model
Enamul Kabir, Hua Wang, Elisa Bertino, Yunxiang Chi · 2010
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 ap-proach to protect data privacy. We recently pro-posed a systematic clustering1 method based on k-anonymization technique that minimizes the informa-tion loss and at the same time assures data quality. In this paper, we extended our previous work on the sys-tematic clustering method to l-diversity model that assumes that every group of indistinguishable records contains at least l distinct sensitive attributes val-ues. 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 im-plemented in two steps, namely clustering step for k-anonymization and l-diverse step. Finally, two algo-rithms of the proposed problem in two steps are devel-oped and shown that the time complexity is in O(n 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.