A Fixed Structure Learning Automaton Micro-aggregation Technique for Secure Statistical Databases

We consider the problem of securing statistical databases and, more specifically, the micro-aggregation technique (MAT), which coalesces the individual records in the micro-data file into groups or classes, and on being queried, reports, for the all individual values, the aggregated means of the corresponding group. This problem is known to be NP-hard and has been tackled using many heuristic solutions. In this paper we present the first reported Learning Automaton (LA) based solution to the MAT. The LA modifies a fixed-structure solution to the Equi-Partitioning Problem (EPP) to solve the micro-aggregation problem. The scheme has been implemented, rigorously tested and evaluated for different real and simulated data sets. The results clearly demonstrate the applicability of LA to the micro-aggregation problem, and to yield a solution that obtains a lower information loss when compared to the best available heuristic methods for micro-aggregation E. Fayyoumi, and B. J. Oommen, “A Fixed Structure Learning Automaton MicroAggregation Technique for Secure Statistical Databases”, Proceedings of PSD’06, Privacy in Sta s cal Databases, Italy: Rome, 13‐15 Dec. 2006, LNCS 4058, Springer Verlag (2006).