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 privacy budget, $\varepsilon$. We provide a framework for setting $\varepsilon$ based on its relationship with Bayesian posterior probabilities of disclosure. The agency responsible for the data release decides how much posterior risk it is willing to accept at various levels of prior risk, which implies a unique $\varepsilon$. Agencies can evaluate different risk profiles to determine one that leads to an acceptable trade-off in risk and utility.

翻译：在发布机密数据的输出结果时，机构需要在发布数据的分析效用与保护数据主体机密性的义务之间取得平衡。对于满足差分隐私的发布，这种平衡通过隐私预算 $\varepsilon$ 来体现。我们提出了一个基于 $\varepsilon$ 与贝叶斯后验披露概率之间关系的框架来设定 $\varepsilon$。负责数据发布的机构决定在不同先验风险水平下愿意接受多少后验风险，这隐含了一个唯一的 $\varepsilon$。机构可以评估不同的风险曲线，以确定一种能在风险与效用之间达成可接受权衡的方案。