A Survey on Differential Privacy for Medical Data Analysis
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...
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| Published in | Annals of data science Vol. 11; no. 2; pp. 733 - 747 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.04.2024
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2198-5804 2198-5812 2198-5812 |
| DOI | 10.1007/s40745-023-00475-3 |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. |
| Author | Liu, WeiKang Meng, Qinxue Yang, Hong Zhang, Yanchun |
| Author_xml | – sequence: 1 givenname: WeiKang surname: Liu fullname: Liu, WeiKang email: dpstudier@e.gzhu.edu.cn organization: Cyberspace Institute of Advanced Technology, Guangzhou University – sequence: 2 givenname: Yanchun surname: Zhang fullname: Zhang, Yanchun organization: Cyberspace Institute of Advanced Technology, Guangzhou University, Institute of Sustainable Industries and Liveable Cities, Victoria University – sequence: 3 givenname: Hong surname: Yang fullname: Yang, Hong organization: Cyberspace Institute of Advanced Technology, Guangzhou University – sequence: 4 givenname: Qinxue surname: Meng fullname: Meng, Qinxue organization: College of Information Engineering, Suzhou University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38625247$$D View this record in MEDLINE/PubMed |
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| Keywords | Medical data Differential privacy Data publishing Privacy computing |
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| SubjectTerms | 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 |
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| Title | A Survey on Differential Privacy for Medical Data Analysis |
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