Theoretical and applied research into privacy encompasses an incredibly broad swathe of differing approaches, emphasis and aims. This work introduces a new quantitative notion of privacy that is both contextual and specific. We argue that it provides a more meaningful notion of privacy than the widely utilised framework of differential privacy and a more explicit and rigorous formulation than what is commonly used in statistical disclosure theory. Our definition relies on concepts inherent to standard Bayesian decision theory, while departing from it in several important respects. In particular, the party controlling the release of sensitive information should make disclosure decisions from the prior viewpoint, rather than conditional on the data, even when the data is itself observed. Illuminating toy examples and computational methods are discussed in high detail in order to highlight the specificities of the method.

翻译：隐私的理论与应用研究涵盖了极其广泛的不同方法、侧重点和目标。本文提出了一种新的定量隐私概念，该概念兼具情境性与特异性。我们认为，这一概念比广泛使用的差分隐私框架提供了更具实质意义的隐私定义，同时也比统计披露理论中常用的表述更为明确和严谨。我们的定义基于标准贝叶斯决策理论的内在概念，但在若干重要方面与之相异。具体而言，控制敏感信息发布的一方应从先验视角做出披露决策，而非基于数据条件进行判断——即使数据本身已被观测。文中通过详尽的示例模型与计算方法讨论，以凸显该方法的具体特性。