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Anonymization for privacy-preserving data publishing, also known asstatistical disclosure control (SDC), can be viewed under the lens of thepermutation model. According to this model, any SDC method for individual datarecords is functionally equivalent to a permutation step plus a noise additionstep, where the noise added is marginal, in the sense that it does not alterranks. Here, we propose metrics to quantify the data confidentiality andutility achieved by SDC methods based on the permutation model. We distinguishtwo privacy notions: in our work, anonymity refers to subjects and hence mainlyto protection against record re-identification, whereas confidentiality refersto the protection afforded to attribute values against attribute disclosure.Thus, our confidentiality metrics are useful even if using a privacy modelensuring an anonymity level ex ante. The utility metric is a general-purposemetric that can be conveniently traded off against the confidentiality metrics,because all of them are bounded between 0 and 1. As an application, we comparethe utility-confidentiality trade-offs achieved by several anonymizationapproaches, including privacy models (k-anonymity and $\epsilon$-differentialprivacy) as well as SDC methods (additive noise, multiplicative noise andsynthetic data) used without privacy models.

General Confidentiality and Utility Metrics for Privacy-Preserving Data Publishing Based on the Permutation Model