论文标题
通过对敏感数据的数据依赖性来防止推断
Preventing Inferences through Data Dependencies on Sensitive Data
论文作者
论文摘要
只需将计算限制为数据的非敏感部分，就可以通过数据依赖性来推断敏感数据。在先前的工作中已经研究了来自数据依赖性的推理控制。但是，现有的解决方案要么检测并拒绝可能导致泄漏的查询 - 导致效用差，或者仅保护敏感数据的精确重建 - 导致安全性差。在本文中，我们提出了一种新颖的安全模型，称为“完全可否认”。在这个更强大的安全模型下，从非敏感数据中推断出有关敏感数据的任何信息都被视为泄漏。我们描述了用于在给定数据库实例上有效实现具有一组数据依赖性和敏感单元格的算法。使用两个不同数据集上的实验，我们证明我们的方法可以保护逼真的对手，同时仅隐藏最少数量的其他非敏感单元格，并且与数据库大小和敏感数据相比，可以很好地缩放。
Simply restricting the computation to non-sensitive part of the data may lead to inferences on sensitive data through data dependencies. Inference control from data dependencies has been studied in the prior work. However, existing solutions either detect and deny queries which may lead to leakage -- resulting in poor utility, or only protects against exact reconstruction of the sensitive data -- resulting in poor security. In this paper, we present a novel security model called full deniability. Under this stronger security model, any information inferred about sensitive data from non-sensitive data is considered as a leakage. We describe algorithms for efficiently implementing full deniability on a given database instance with a set of data dependencies and sensitive cells. Using experiments on two different datasets, we demonstrate that our approach protects against realistic adversaries while hiding only minimal number of additional non-sensitive cells and scales well with database size and sensitive data.
