Desmet Chance, Cook Diane J
Washington State University.
ACM IMS Trans Data Sci. 2021 Nov;2(4). doi: 10.1145/3447774.
With the dramatic increases in both the capability to collect personal data and the capability to analyze large amounts of data, increasingly sophisticated and personal insights are being drawn. These insights are valuable for clinical applications but also open up possibilities for identification and abuse of personal information. In this paper, we survey recent research on classical methods of privacy-preserving data mining. Looking at dominant techniques and recent innovations to them, we examine the applicability of these methods to the privacy-preserving analysis of clinical data. We also discuss promising directions for future research in this area.
随着收集个人数据能力和分析大量数据能力的急剧增长，人们得出了越来越复杂和个性化的见解。这些见解对临床应用很有价值，但也为个人信息的识别和滥用带来了可能性。在本文中，我们综述了关于隐私保护数据挖掘经典方法的近期研究。审视主导技术及其近期创新，我们考察了这些方法在临床数据隐私保护分析中的适用性。我们还讨论了该领域未来研究的有前景的方向。
