A variable-MDAV-based partitioning strategy to continuous multivariate microaggregation with genetic algorithms

Microaggregation is a Statistical Disclosure Control (SDC) technique that aims at protecting the privacy of individual respondents before their data are released. Optimally microaggregating multivariate data sets is known to be an NP-hard problem. Thus, using heuristics has been suggested as a possible strategy to tackle it. Specifically, Genetic Algorithms have been shown to be serious candidates that can find good solutions on small data sets. However, due to the very nature of these algorithms and the coding of the microaggregation problem, GA can hardly cope with large data sets. In order to apply them to large data sets, the latter have to be previously partitioned into smaller disjoint subsets that the GA can handle. In this article we summarise several proposals for partitioning data sets, in order to use GA to microaggregate them. In addition, we suggest a new partitioning strategy based on the variable-MDAV algorithm, and we compare it with the most relevant previous proposals. The experimental results show that our method outperforms the previous ones in terms of information loss.