  In the field of statistical disclosure control, the tradeoff between data
confidentiality and data utility is measured by comparing disclosure risk and
information loss metrics. Distance based metrics such as the mean absolute
error (MAE), mean squared error (MSE), mean variation (IL1), and its scaled
alternative (IL1s) are popular information loss measures for numerical
microdata. However, the fact that these measures are unbounded makes it is
difficult to compare them against disclosure risk measures which are usually
bounded between 0 and 1. In this note, we propose rank-based versions of the
MAE and MSE metrics that are bounded in the same range as the disclosure risk
metrics. We empirically compare the proposed bounded metrics against the
distance-based metrics in a series of experiments where the metrics are
evaluated over multiple masked datasets, generated by the application of
increasing amounts of perturbation (e.g., by adding increasing amounts of
noise). Our results show that the proposed bounded metrics produce similar
rankings as the traditional ones (as measured by Spearman correlation),
suggesting that they are a viable additions to the toolbox of distance-based
information loss metrics currently in use in the SDC literature.
