  When releasing outputs from confidential data, agencies need to balance the
analytical usefulness of the released data with the obligation to protect data
subjects' confidentiality. For releases satisfying differential privacy, this
balance is reflected by the parameter epsilon, known as the privacy budget. In
practice, it can be difficult for agencies to select and interpret epsilon. We
use Bayesian posterior probabilities of disclosure to provide a framework for
setting epsilon. The agency decides how much posterior risk it is willing to
accept in a data release at various levels of prior risk. Using a mathematical
relationship among these probabilities and epsilon, the agency selects the
maximum epsilon that ensures the posterior-to-prior ratios are acceptable for
all values of prior disclosure risk. The framework applies to any
differentially private mechanism.

Cryptography and Security;Methodology