Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Introduction to algorithms
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
Aggregation techniques for statistical confidentiality
Aggregation operators
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
ICICS'07 Proceedings of the 9th international conference on Information and communications 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
A fixed structure learning automaton micro-aggregation technique for secure statistical databases
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
Software—Practice & Experience - Focus on Selected PhD Literature Reviews in the Practical Aspects of Software Technology
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We consider the Micro-Aggregation Problem (MAP) in secure statistical databases which involves partitioning a set of individual records in a micro-data file into a number of mutually exclusive and exhaustive groups. This problem, which seeks for the best partition of the micro-data file, is known to be NP-hard, and has been tackled using many heuristic solutions. In this paper, we would like to demonstrate that in the process of developing Micro-Aggregation Techniques (MATs), it is expedient to incorporate information about the dependence between the random variables in the micro-data file. This can be achieved by pre-processing the micro-data beforeinvoking any MAT, in order to extract the useful dependence information from the joint probability distribution of the variables in the micro-data file, and then accomplishing the micro-aggregation on the "maximally independent" variables. Our results, on real life data sets, show that including such information will enhance the process of determining how many variables are to be used, and which of them should be used in the micro-aggregation process.