Nicolas Ruiz

On some consequences of the permutation paradigm for data anonymization: Centrality of permutation matrices, universal measures of disclosure risk and information loss, evaluation by dominance

Abstract Recently, the permutation paradigm has been proposed in data anonymization to describe any micro data masking method as permutation, paving the way for performing meaningful analytical comparisons of methods, something that is difficult currently in statistical disclosure control research. This paper explores some consequences of this paradigm by establishing some class of universal measures of disclosure risk and information loss that can be used for the evaluation and comparison of any method on most data, under any parametrization and independently of the characteristics of the data to be anonymized. These measures lead to the introduction in data anonymization of the concepts of dominance in disclosure risk and information loss, which formalize the fact that different parties involved in micro data transaction can all have different sensitivities to privacy and information.

Going beyond GDP: empirical findings

The chapter presents an overview of several challenging issues related to the assessment of well-being through measures aimed at looking ‘beyond GDP’. Although designed to measure aggregate economic performance from a macroeconomic perspective, gross domestoc product (GDP) has been extensively used to measure welfare, with a number of problems and limitations. Starting from the mid-1970s, criticisms to this approach encouraged early attempts to create alternative measures for GDP. The Great Recession and inequality considerations further prompted the discussion through national and international initiatives. These gave birth to a set of measures and frameworks which focus more on the individuals, entailing considerations on the distribution of well-being, multidimensionality and subjective perceptions. The chapter reviews some of these measures, addressing the main issues and techniques as well as outlining the greatest statistical challenges linked to the measurement of progress and well-being.