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A Survey on Differential Privacy for Medical Data Analysis
WeiKang Liu1, Yanchun Zhang1,2, Hong Yang1
1Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China.
This survey explores differential privacy techniques for securing sensitive medical data in machine learning applications. It highlights the importance of privacy preservation for genomic and wearable device data analysis.
Area of Science:
- Medical Informatics
- Data Security
- Machine Learning
Background:
- Machine learning enhances medical data analysis, but raises privacy concerns, especially during data sharing.
- Protecting sensitive patient information is crucial for the trustworthy application of medical data.
Purpose of the Study:
- To survey the application of differential privacy in medical data analysis.
- To discuss the necessity and benefits of privacy-preserving techniques in medicine.
Main Methods:
- Review of existing research on differential privacy in healthcare.
- Analysis of differential privacy applications for genomic and wearable device data.
Main Results:
- Differential privacy offers a robust framework for protecting sensitive medical data.
- Identified key applications and challenges in implementing these privacy strategies.
Conclusions:
- Differential privacy is essential for secure medical data analysis and sharing.
- Future research should address current challenges to further advance privacy in medical AI.
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