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A derivative-free algorithm for refining numerical microaggregation solutions
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DOI：10.1111/itor.12125.png)
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Abstract
En 中文
The biggest challenge in disclosing private data is to share information contained in databases, while protecting people from being individually identified. Microaggregation is a family of methods for statistical disclosure control. The principle of microaggregation states that confidentiality rules permit the publication of individual records if they are divided into groups of g or more data, where none of the records is more representative than the others in the same group. The application of such rules leads to replacing individual values by those computed from small groups, which are denoted as microaggregates, before data publication. The microaggregation procedure should be developed to reduce the information loss caused by this replacement process to an extent, so that valuable and accurate information could still be extracted from the microaggregated data. This work proposes a derivative-free optimization algorithm for the numerical microaggregation problem based on the evaluation of a simplified surrogate function. Computational experiments show that the proposed method can considerably improve the results obtained by other heuristics found in the literature, as it improves some of its best-known results.
Keywords:
data privacy
microaggregation
derivative-free optimization
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