Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
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
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
ACISP '08 Proceedings of the 13th Australasian conference on Information Security and Privacy
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
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
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We consider the problem of securing a statistical database by utilizing the well-known micro-aggregation strategy, and in particular, the k-Ward strategy introduced in [1] and utilized in [2]. The latter scheme, which represents the state-of-the-art, coalesces the sorted data attribute values into groups, and on being queried, reports the means of the corresponding groups. We demonstrate that such a scheme can be optimized on two fronts. First of all, we minimize the computations done in evaluating the between-class distance matrix, to require only a constant number of updating distance computations. Secondly, and more importantly, we propose that the data set be partitioned recursively before a k-Ward strategy is invoked, and that the latter be invoked on the “primitive” sub-groups which terminate the recursion. Our experimental results, done on two benchmark data sets, demonstrate a marked improvement. While the information loss is comparable to the k-Ward micro-aggregation technique proposed by Domingo-Ferrer et.al. [2], the computations required to achieve this loss is a fraction of the computations required in the latter – providing a computational advantage which sometimes exceeds 80% if one method is used by itself, and more than 90% if both enhancements are invoked simultaneously.