论文标题
在差异隐私中关联数据：定义和分析
Correlated Data in Differential Privacy: Definition and Analysis
论文作者
论文摘要
差异隐私是一个严格的数学框架，用于评估和保护数据隐私。在大多数现有研究中，存在一个脆弱的假设，即应用差异隐私时，数据集中的记录是独立的。但是，在实际数据集中，记录可能会相关联，这可能会导致意外的数据泄漏。在这项调查中，我们研究了由于差异隐私模型下的数据相关性而导致的隐私损失问题。粗略地，我们将现有文献分为三行：1）使用参数描述差异隐私中的数据相关性，2）使用模型来描述差异隐私中的数据相关性，3）根据Pufferfish的框架描述数据相关性。首先，给出了一个详细的示例，以说明在真实场景中相关数据上的隐私泄漏问题。然后，我们的主要工作是分析和比较这些方法，并评估应用这些不同研究的情况。最后，我们提出了有关相关差异隐私的一些未来挑战。
Differential privacy is a rigorous mathematical framework for evaluating and protecting data privacy. In most existing studies, there is a vulnerable assumption that records in a dataset are independent when differential privacy is applied. However, in real-world datasets, records are likely to be correlated, which may lead to unexpected data leakage. In this survey, we investigate the issue of privacy loss due to data correlation under differential privacy models. Roughly, we classify existing literature into three lines: 1) using parameters to describe data correlation in differential privacy, 2) using models to describe data correlation in differential privacy, and 3) describing data correlation based on the framework of Pufferfish. Firstly, a detailed example is given to illustrate the issue of privacy leakage on correlated data in real scenes. Then our main work is to analyze and compare these methods, and evaluate situations that these diverse studies are applied. Finally, we propose some future challenges on correlated differential privacy.
