We consider the microaggregation problem (MAP) that involves partitioning a set of individual records in a microdata file into a number of mutually exclusive and exhaustive groups. This problem, which seeks for the best partition of the microdata file, is known to be NP-hard and has been tackled using many heuristic solutions. In this paper, we present the first reported fixed-structure-stochastic-automata -based solution to this problem. The newly proposed method leads to a lower value of the information loss (IL), obtains a better tradeoff between the IL and the disclosure risk (DR) when compared with state-of-the-art methods, and leads to a superior value of the scoring index, which is a criterion involving a combination of the IL and the DR. The scheme has been implemented, tested, and evaluated for different real-life and simulated data sets. The results clearly demonstrate the applicability of learning automata to the MAP and its ability to yield a solution that obtains the best tradeoff between IL and DR when compared with the state of the art.

Additional Metadata
Keywords Disclosure risk (DR), Information loss (IL), Microaggregation technique (MAT), Object migrating microaggregated automaton (OMMA)
Persistent URL dx.doi.org/10.1109/TSMCB.2009.2013723
Journal IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fayyoumi, E. (Ebaa), & Oommen, J. (2009). Achieving microaggregation for secure statistical databases using fixed-structure partitioning-based learning automata. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 39(5), 1192–1205. doi:10.1109/TSMCB.2009.2013723