Healthcare has seen considerable technology improvements recently, including the development of online clinical research applications, electronic health records, and data collection from mobile devices, wearables, and patient-scale behavioural studies. Healthcare organization’s efficiency has increased significantly due to these developments, but they have also generated questions regarding data security and privacy. When acquired by conventional survey and questionnaire techniques, collecting personal health data was formerly considered of personal health data was formerly seen to be low-risk. However, as this data progressively populates computerized databases with sensitive information from many sources, it has become a privacy concern. Data breaches in the healthcare sector have shown how crucial it is to secure personal information. The emergency requirement for the healthcare industry to strike a balance between data privacy and usefulness has prompted the investigation of creative solutions. Federated learning has great potential since it enables different users to work together to train a single model without centralized data management. However, maintaining data value while obtaining strong data privacy continues to be daunting. In order to resolve the trade-offs between privacy and usefulness, this chapter investigates ways to protect privacy in federated learning for healthcare data analysis. This chapter’s primary goal is to thoroughly evaluate current methods for tackling the privacy-utility trade-off in federated learning-based healthcare data analysis. In order to improve data privacy while preserving the usability and efficacy of healthcare data, it is crucial to discover and evaluate different methodologies, tactics, and use cases that may provide solutions. This chapter examines the present state of privacy issues in healthcare data analysis, highlighting the need to protect private patient data and explores federated learning as a viable strategy to allay these worries and highlight its potential uses in the healthcare industry. Additionally, the chapter highlights various use cases while considering various factors that may help us strike a better balance between data privacy and data value in the healthcare industry. In conclusion, by emphasizing federated learning as a crucial solution to solve these issues, this chapter adds to the continuing conversation on data privacy in healthcare. The chapter provides insights into how healthcare organizations may strengthen data privacy while using the plethora of information available to better patient care, medical research, and operational efficiency by analyzing current methodologies and investigating creative use cases. In the end, preserving public confidence and developing the field of healthcare informatics depend on protecting privacy in federated learning for healthcare data analysis.

Privacy Protection for Medical Information in Federated Learning

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发表日期：

2025年

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Malyala Vaishnavi,
Srikanth Vemuru,
Afsana Khan

摘要

Healthcare has seen considerable technology improvements recently, including the development of online clinical research applications, electronic health records, and data collection from mobile devices, wearables, and patient-scale behavioural studies. Healthcare organization’s efficiency has increased significantly due to these developments, but they have also generated questions regarding data security and privacy. When acquired by conventional survey and questionnaire techniques, collecting personal health data was formerly considered of personal health data was formerly seen to be low-risk. However, as this data progressively populates computerized databases with sensitive information from many sources, it has become a privacy concern. Data breaches in the healthcare sector have shown how crucial it is to secure personal information. The emergency requirement for the healthcare industry to strike a balance between data privacy and usefulness has prompted the investigation of creative solutions. Federated learning has great potential since it enables different users to work together to train a single model without centralized data management. However, maintaining data value while obtaining strong data privacy continues to be daunting. In order to resolve the trade-offs between privacy and usefulness, this chapter investigates ways to protect privacy in federated learning for healthcare data analysis. This chapter’s primary goal is to thoroughly evaluate current methods for tackling the privacy-utility trade-off in federated learning-based healthcare data analysis. In order to improve data privacy while preserving the usability and efficacy of healthcare data, it is crucial to discover and evaluate different methodologies, tactics, and use cases that may provide solutions. This chapter examines the present state of privacy issues in healthcare data analysis, highlighting the need to protect private patient data and explores federated learning as a viable strategy to allay these worries and highlight its potential uses in the healthcare industry. Additionally, the chapter highlights various use cases while considering various factors that may help us strike a better balance between data privacy and data value in the healthcare industry. In conclusion, by emphasizing federated learning as a crucial solution to solve these issues, this chapter adds to the continuing conversation on data privacy in healthcare. The chapter provides insights into how healthcare organizations may strengthen data privacy while using the plethora of information available to better patient care, medical research, and operational efficiency by analyzing current methodologies and investigating creative use cases. In the end, preserving public confidence and developing the field of healthcare informatics depend on protecting privacy in federated learning for healthcare data analysis.