Bayesian privacy
提出用贝叶斯方法衡量机制中的隐私损失，即设计者先验与后验信念的Kullback-Leibler散度，并应用于垄断筛选模型，研究隐私约束下的最优机制及福利与隐私的关系。
Modern information technologies make it possible to store, analyze, and trade unprecedented amounts of detailed information about individuals. This has led to public discussions on whether individuals' privacy should be better protected by restricting the amount or the precision of information that is collected by commercial institutions on their participants. We contribute to this discussion by proposing a Bayesian approach to measure loss of privacy in a mechanism. Specifically, we define the loss of privacy associated with a mechanism as the difference between the designer's prior and posterior beliefs about an agent's type, where this difference is calculated using Kullback–Leibler divergence, and where the change in beliefs is triggered by actions taken by the agent in the mechanism. We consider both ex post (for every realized type, the maximal difference in beliefs cannot exceed some threshold κ ) and ex ante (the expected difference in beliefs over all type realizations cannot exceed some threshold κ ) measures of privacy loss. Applying these notions to the monopolistic screening environment of Mussa and Rosen (1978), we study the properties of optimal privacy‐constrained mechanisms and the relation between welfare/profits and privacy levels.