Pufferfish: A Framework for Mathematical Privacy Definitions
Pufferfish: A Framework for Mathematical Privacy Definitions
复制标题
DOI：
10.1145/2514689
复制
发表时间：
2014-01-01
影响因子：
1.8
通讯作者：
Machanavajjhala, Ashwin
中科院分区：
文献类型：
--
作者：
Kifer, Daniel;Machanavajjhala, Ashwin
在本文中，我们将介绍一个新的通用隐私框架，称为Pufferfish。Pufferfish框架可以用来创建新的隐私定义，这些定义可以根据给定应用程序的需求进行定制。Pufferfish的目标是让应用领域的专家（他们通常不具备隐私方面的专业知识）为他们的数据共享需求制定严格的隐私定义。除此之外，Pufferfish框架还可以用来研究现有的隐私定义，我们用这个隐私框架的几个应用来说明它的好处：我们用它来分析差分隐私，并形式化与那些认为数据记录是独立的攻击者的联系;我们用它来创建一个隐私定义，称为对冲隐私，它可以用来排除那些先前的信念与数据不一致的攻击者;我们使用的框架定义和研究的概念组成在一个更广泛的背景下比以前，我们展示了如何应用框架来保护无界的连续属性和聚合信息，我们展示了如何使用框架来严格考虑以前的数据发布。
In this article, we introduce a new and general privacy framework called Pufferfish. The Pufferfish framework can be used to create new privacy definitions that are customized to the needs of a given application. The goal of Pufferfish is to allow experts in an application domain, who frequently do not have expertise in privacy, to develop rigorous privacy definitions for their data sharing needs. In addition to this, the Pufferfish framework can also be used to study existing privacy definitions.We illustrate the benefits with several applications of this privacy framework: we use it to analyze differential privacy and formalize a connection to attackers who believe that the data records are independent; we use it to create a privacy definition called hedging privacy, which can be used to rule out attackers whose prior beliefs are inconsistent with the data; we use the framework to define and study the notion of composition in a broader context than before; we show how to apply the framework to protect unbounded continuous attributes and aggregate information; and we show how to use the framework to rigorously account for prior data releases.