Information Loss in Continuous Hybrid Microdata: Subdomain-Level Probabilistic Measures

The goal of privacy protection in statistical databases is to balance the social right to know and the individual right to privacy. When microdata (i.e. data on individual respondents) are released, they should stay analytically useful but should be protected so that it cannot be decided whether a published record matches a specific individual. However, there is some uncertainty in the assessment of data utility, since the specific data uses of the released data cannot always be anticipated by the data protector. Also, there is uncertainty in assessing disclosure risk, because the data protector cannot foresee what will be the information context of potential intruders. Generating synthetic microdata is an alternative to the usual approach based on distorting the original data. The main advantage is that no original data are released, so no disclosure can happen. However, subdomains (i.e. subsets of records) of synthetic datasets do not resemble the corresponding subdomains of the original dataset. Hybrid microdata mixing original and synthetic microdata overcome this lack of analytical validity. We present a fast method for generating numerical hybrid microdata in a way that preserves attribute means, variances and covariances, as well as (to some extent) record similarity and subdomain analyses. We also overcome the uncertainty in assessing data utility by using newly defined probabilistic information loss measures.