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Organizations releasing public or restricted use files attempt to minimize disclosure risks for participants while maintaining data utility. This presentation reviews the disclosure risk assessment for the 2016 Survey of Prison Inmates (SPI). SPI routinely collects identifiable and sensitive information such as health histories and offense histories from prisoners. Surveys of confined populations, such as prisoners, pose higher disclosure risk challenges than general population surveys. Populations in known, confined locations - like prisons - are more vulnerable to identification. Thus, we sought to determine the methods that best encapsulate the trade-off between data utility and minimization of disclosure risk. Through a variety of assessments of frequencies and uniqueness we discern how easily one can identify certain groups and individuals. We considered non-perturbative and disclosure avoidance methods such as coarsening and suppression to decrease disclosure risk. For each method we evaluate the trade-off and propose how methods presented here could also be applied to other surveys with hierarchal groups such as schools or hospitals.
