| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP13536 |
| DP13536 Optimal Privacy-Constrained Mechanisms | |
| Kfir Eliaz; Ran Eilat; Xiaosheng Mu | |
| 发表日期 | 2019-02-15 |
| 出版年 | 2019 |
| 语种 | 英语 |
| 摘要 | 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 its participants. We contribute to this discussion by proposing a Bayesian approach to measure loss of privacy and applying it to the design of optimal mechanisms. 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-ante (the expected difference in beliefs over all type realizations cannot exceed some threshold κ) and ex-post (for every realized type, the maximal difference in beliefs cannot exceed some threshold κ) measures of privacy loss. Using these notions we study the properties of optimal privacy-constrained mechanisms and the relation between welfare/profits and privacy levels. |
| 主题 | Industrial Organization |
| 关键词 | Privacy Mechanism-design |
| URL | https://cepr.org/publications/dp13536 |
| 来源智库 | Centre for Economic Policy Research (United Kingdom) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/542352 |
| 推荐引用方式 GB/T 7714 | Kfir Eliaz,Ran Eilat,Xiaosheng Mu. DP13536 Optimal Privacy-Constrained Mechanisms. 2019. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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