A Privacy-Protection Model for Patients

The collection and analysis of patient cases can effectively help researchers to extract case feature and to achieve the objectives of precision medicine, but it may cause privacy issues for patients. Although encryption is a good way to protect privacy, it is not conducive to the sharing and analys...

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Bibliographic Details
Published in:Security and communication networks 2020, Vol.2020 (2020), p.1-12
Main Authors: Liu, Dingwan, Yan, Wanqin, Yin, Xiangdong, Ou, Wei, Cheng, Wenzhi, Liu, Chunyan
Format: Article
Language:English
Subjects:
Access control
Algorithms
Blockchain
Cryptography
Data encryption
Data exchange
Decentralization
Digital currencies
Digital signatures
Encryption
Feature extraction
Information sharing
Internet
Machine learning
Medical records
Medical research
Medical technology
Patients
Personal health
Personal information
Precision medicine
Privacy
Probabilistic models
Security
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Summary:The collection and analysis of patient cases can effectively help researchers to extract case feature and to achieve the objectives of precision medicine, but it may cause privacy issues for patients. Although encryption is a good way to protect privacy, it is not conducive to the sharing and analysis of medical cases. In order to address this problem, this paper proposes a federated learning verification model, which combines blockchain technology, homomorphic encryption, and federated learning technology to effectively solve privacy issues. Moreover, we present a FL-EM-GMM Algorithm (Federated Learning Expectation Maximization Gaussian Mixture Model Algorithm), which can make model training without data exchange for protecting patient’s privacy. Finally, we conducted experiments on the federated task of datasets from two organizations in our model system, where the data has the same sample ID with different subset features, and this system is capable of handling privacy and security issues. The results show that the model was trained by our system with better usability, security, and higher efficiency, which is compared with the model trained by traditional machine learning methods.
ISSN:1939-0114
1939-0122
DOI:10.1155/2020/6647562