A Survey on Differential Privacy for Medical Data Analysis

• Published in: Annals of data science Vol. 11; no. 2; pp. 733 - 747
• Main Authors: Liu, WeiKang, Zhang, Yanchun, Yang, Hong, Meng, Qinxue
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Business and Management; Correlation analysis; Data analysis; Data integrity; Economics; Finance; Information technology; Insurance; Machine learning; Management; Mining; Privacy; Statistics for Business; Sustainable development; Wearable technology; Medical data; Differential privacy; Data publishing; Privacy computing
• ISSN: 2198-5804; 2198-5812; 2198-5812
• DOI: 10.1007/s40745-023-00475-3

Machine learning methods promote the sustainable development of wise information technology of medicine (WITMED), and a variety of medical data brings high value and convenience to medical analysis. However, the applications of medical data have also been confronted with the risk of privacy leakage that is hard to avoid, especially when conducting correlation analysis or data sharing among multiple institutions. Data security and privacy preservation have recently played an essential role in the field of secure and private medical data analysis, where many differential privacy strategies are applied to medical data publishing and mining. In this paper, we survey research work on the applications of differential privacy for medical data analysis, discussing the necessity of medical privacy-preserving, the advantages of differential privacy, and their applications to typical medical data, such as genomic data and wearable device data. Furthermore, we discuss the challenges and potential future research directions for differential privacy in medical applications.