Fu Ada Wai-Chee, Wang Ke, Wong Raymond Chi-Wing, Wang Jia, Jiang Minhao
Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong.
Department of Computer Science, Simon Fraser University, Canada.
J Biomed Inform. 2014 Aug;50:20-31. doi: 10.1016/j.jbi.2014.04.002. Epub 2014 Apr 13.
While the study of privacy preserving data publishing has drawn a lot of interest, some recent work has shown that existing mechanisms do not limit all inferences about individuals. This paper is a positive note in response to this finding. We point out that not all inference attacks should be countered, in contrast to all existing works known to us, and based on this we propose a model called SPLU. This model protects sensitive information, by which we refer to answers for aggregate queries with small sums, while queries with large sums are answered with higher accuracy. Using SPLU, we introduce a sanitization algorithm to protect data while maintaining high data utility for queries with large sums. Empirical results show that our method behaves as desired.
虽然隐私保护数据发布的研究引起了广泛关注，但最近的一些工作表明，现有机制并不能限制对个体的所有推断。本文是对这一发现的积极回应。与我们所知的所有现有工作不同，我们指出并非所有推断攻击都应被对抗，基于此我们提出了一个名为SPLU的模型。该模型保护敏感信息，我们所说的敏感信息是指总和较小的聚合查询的答案，而对于总和较大的查询则以更高的准确性进行回答。使用SPLU，我们引入了一种净化算法来保护数据，同时保持对总和较大的查询的高数据效用。实证结果表明我们的方法表现如预期。
