Marginality: a numerical mapping for enhanced treatment of nominal and hierarchical attributes

TL;DR

This paper introduces a numerical mapping for hierarchical nominal data to enable statistical analysis techniques like computing means and variances, improving data anonymization methods for categorical data.

Contribution

The paper proposes a novel numerical mapping for hierarchical nominal attributes, facilitating statistical analysis and enhancing privacy-preserving data publishing.

Findings

Enables calculation of means, variances, covariances for nominal data

Improves data anonymization techniques for categorical data

Expands options for statistical disclosure control methods

Abstract

The purpose of statistical disclosure control (SDC) of microdata, a.k.a. data anonymization or privacy-preserving data mining, is to publish data sets containing the answers of individual respondents in such a way that the respondents corresponding to the released records cannot be re-identified and the released data are analytically useful. SDC methods are either based on masking the original data, generating synthetic versions of them or creating hybrid versions by combining original and synthetic data. The choice of SDC methods for categorical data, especially nominal data, is much smaller than the choice of methods for numerical data. We mitigate this problem by introducing a numerical mapping for hierarchical nominal data which allows computing means, variances and covariances on them.