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In the field of statistical disclosure control, the tradeoff between dataconfidentiality and data utility is measured by comparing disclosure risk andinformation loss metrics. Distance based metrics such as the mean absoluteerror (MAE), mean squared error (MSE), mean variation (IL1), and its scaledalternative (IL1s) are popular information loss measures for numericalmicrodata. However, the fact that these measures are unbounded makes it isdifficult to compare them against disclosure risk measures which are usuallybounded between 0 and 1. In this note, we propose rank-based versions of theMAE and MSE metrics that are bounded in the same range as the disclosure riskmetrics. We empirically compare the proposed bounded metrics against thedistance-based metrics in a series of experiments where the metrics areevaluated over multiple masked datasets, generated by the application ofincreasing amounts of perturbation (e.g., by adding increasing amounts ofnoise). Our results show that the proposed bounded metrics produce similarrankings as the traditional ones (as measured by Spearman correlation),suggesting that they are a viable additions to the toolbox of distance-basedinformation loss metrics currently in use in the SDC literature.

A note on bounded distance-based information loss metrics for statistical disclosure control of numeric microdata