An in-depth examination of requirements for disclosure risk assessment.

The use of formal privacy to protect the confidentiality of responses in the 2020 Decennial Census of Population and Housing has triggered renewed interest and debate over how to measure the disclosure risks and societal benefits of the published data products. We argue that any proposal for quantifying disclosure risk should be based on prespecified, objective criteria. We illustrate this approach to evaluate the absolute disclosure risk framework, the counterfactual framework underlying differential privacy, and prior-to-posterior comparisons. We conclude that satisfying all the desiderata is impossible, but counterfactual comparisons satisfy the most while absolute disclosure risk satisfies the fewest. Furthermore, we explain that many of the criticisms levied against differential privacy would be levied against any technology that is not equivalent to direct, unrestricted access to confidential data. More research is needed, but in the near term, the counterfactual approach appears best-suited for privacy versus utility analysis.

The cross-classified multilevel measurement model: an explanation and demonstration.

The link between the hierarchical generalized linear model (HGLM) and the Rasch model's parameterization has already been demonstrated by several researchers. Extensions have been described that include higher clustering levels to model more appropriately the contextual effects that are frequently encountered in educational research. However, pure hierarchies are relatively rare and instead cross-classified data structures are more frequently encountered. Cross-classified random effect modeling (CCREM) is still not commonly used. Use of CCREM in combination with the multilevel measurement model (MMM) has been recently introduced and is described further in the current study. Specifically, the link between the MMM and the CCREM MMM (termed "CCMMM" model) is provided. A dataset was simulated to demonstrate interpretation of the CCMMM model's parameters and to compare results under a CCMMM versus HGLM analysis. An Appendix is provided to demonstrate SAS GLIMMIX code used to estimate HGLM and CCMMM models' parameters.