Enhancing data sharing via "safe designs"

The social value of data collections are dramatically enhanced by the broad dissemination of research files and the resulting increase in scientific productivity. Currently, most studies are designed with a focus on collecting information that is analytically useful and accurate, with little forethought as to how it will be shared. Both literature and practice also presume that disclosure analysis will take place after data collection. But to produce public-use data of the highest analytical utility for the largest user group, disclosure risk must be considered at the beginning of the research process. Drawing upon economic and statistical decision-theoretic frameworks and survey methodology research, this study seeks to enhance the scientific productivity of shared research data by describing how disclosure risk can be addressed in the earliest stages of research with the formulation of "safe designs" and "disclosure simulations", where an applied statistical approach has been taken in: (1) developing and validating models that predict the composition of survey data under different sampling designs; (2) selecting and/or developing measures and methods used in the assessments of disclosure risk, analytical utility, and disclosure survey costs that are best suited for evaluating sampling and database designs; and (3) conducting simulations to gather estimates of risk, utility, and cost for studies with a wide range of sampling and database design characteristics.