Privacy Preserving Machine Learning for Electronic Health Records using Federated Learning and Differential Privacy
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866910499070803968 |
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| author | Ganadily, Naif A. Xia, Han J. |
| author_facet | Ganadily, Naif A. Xia, Han J. |
| contents | An Electronic Health Record (EHR) is an electronic database used by healthcare providers to store patients' medical records which may include diagnoses, treatments, costs, and other personal information. Machine learning (ML) algorithms can be used to extract and analyze patient data to improve patient care. Patient records contain highly sensitive information, such as social security numbers (SSNs) and residential addresses, which introduces a need to apply privacy-preserving techniques for these ML models using federated learning and differential privacy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_15962 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Privacy Preserving Machine Learning for Electronic Health Records using Federated Learning and Differential Privacy Ganadily, Naif A. Xia, Han J. Machine Learning Cryptography and Security Emerging Technologies An Electronic Health Record (EHR) is an electronic database used by healthcare providers to store patients' medical records which may include diagnoses, treatments, costs, and other personal information. Machine learning (ML) algorithms can be used to extract and analyze patient data to improve patient care. Patient records contain highly sensitive information, such as social security numbers (SSNs) and residential addresses, which introduces a need to apply privacy-preserving techniques for these ML models using federated learning and differential privacy. |
| title | Privacy Preserving Machine Learning for Electronic Health Records using Federated Learning and Differential Privacy |
| topic | Machine Learning Cryptography and Security Emerging Technologies |
| url | https://arxiv.org/abs/2406.15962 |