Deterministic Learning Automata Solutions to the Equipartitioning Problem
IEEE Transactions on Computers
Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Improvements to an Algorithm for Equipartitioning
IEEE Transactions on Computers
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
A Privacy-Enhanced Microaggregation Method
FoIKS '02 Proceedings of the Second International Symposium on Foundations of Information and Knowledge Systems
A Polynomial Algorithm for Optimal Univariate Microaggregation
IEEE Transactions on Knowledge and Data Engineering
Minimum Spanning Tree Partitioning Algorithm for Microaggregation
IEEE Transactions on Knowledge and Data Engineering
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
Data Mining and Knowledge Discovery
A 2^d-Tree-Based Blocking Method for Microaggregating Very Large Data Sets
ARES '06 Proceedings of the First International Conference on Availability, Reliability and Security
On optimizing the k-ward micro-aggregation technique for secure statistical databases
ACISP'06 Proceedings of the 11th Australasian conference on Information Security and Privacy
ACISP '08 Proceedings of the 13th Australasian conference on Information Security and Privacy
An AI-Based Causal Strategy for Securing Statistical Databases Using Micro-aggregation
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ICICS'07 Proceedings of the 9th international conference on Information and communications security
Software—Practice & Experience - Focus on Selected PhD Literature Reviews in the Practical Aspects of Software Technology
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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.