Systematic Clustering-Based Microaggregation for Statistical Disclosure Control

Microdata protection in statistical databases has recently become a major societal concern. Micro aggregation for Statistical Disclosure Control (SDC) is a family of methods to protect microdata from individual identification. Micro aggregation works by partitioning the microdata into groups of at least k records and then replacing the records in each group with the centroid of the group. This paper presents a clustering-based micro aggregation method to minimize the information loss. The proposed technique adopts to group similar records together in a systematic way and then anonymized with the centroid of each group individually. The structure of systematic clustering problem is defined and investigated and an algorithm of the proposed problem is developed. Experimental results show that our method attains a reasonable dominance with respect to both information loss and execution time than the most popular heuristic algorithm called Maximum Distance to Average Vector (MDAV).