论文标题
通过分发隐私机制保护数据集的全球属性
Protecting Global Properties of Datasets with Distribution Privacy Mechanisms
论文作者
论文摘要
我们考虑确保在数据集的许多记录上汇总的数据集属性机密性的问题。这些属性可以编码敏感信息，例如商业秘密或人口统计数据，同时涉及与文献中通常讨论的单个记录的隐私不同的数据保护概念。在这项工作中，我们演示了如何将分发隐私框架应用于形式化此类数据机密性。我们将Wasserstein机制从Pufferfish的隐私和高斯机制扩展到属性隐私到此框架，然后分析其基本数据假设以及如何放松它们。然后，我们从经验上评估了这些机制的隐私 - 实用性权衡，并将其应用于针对数据集全球属性的实用属性推理攻击。结果表明，我们的机制确实可以降低攻击的有效性，同时提供的实用性大大比原油差异隐私基线要大得多。因此，我们的工作为保护数据集的全球性质以及其在实践中的评估提供了理论机制的基础。
We consider the problem of ensuring confidentiality of dataset properties aggregated over many records of a dataset. Such properties can encode sensitive information, such as trade secrets or demographic data, while involving a notion of data protection different to the privacy of individual records typically discussed in the literature. In this work, we demonstrate how a distribution privacy framework can be applied to formalize such data confidentiality. We extend the Wasserstein Mechanism from Pufferfish privacy and the Gaussian Mechanism from attribute privacy to this framework, then analyze their underlying data assumptions and how they can be relaxed. We then empirically evaluate the privacy-utility tradeoffs of these mechanisms and apply them against a practical property inference attack which targets global properties of datasets. The results show that our mechanisms can indeed reduce the effectiveness of the attack while providing utility substantially greater than a crude group differential privacy baseline. Our work thus provides groundwork for theoretical mechanisms for protecting global properties of datasets along with their evaluation in practice.
