Summary of Privacy Preserving Machine Learning For Electronic Health Records Using Federated Learning and Differential Privacy, by Naif A. Ganadily et al.
Privacy Preserving Machine Learning for Electronic Health Records using Federated Learning and Differential Privacy
by Naif A. Ganadily, Han J. Xia
First submitted to arxiv on: 23 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Emerging Technologies (cs.ET)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning algorithms can be used to extract and analyze patient data from Electronic Health Records (EHRs) to improve patient care. Federated learning and differential privacy are applied to ensure the protection of sensitive information, such as social security numbers and residential addresses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary An electronic database called an Electronic Health Record (EHR) is used by healthcare providers to store patients’ medical records. Machine learning algorithms can be used to help improve patient care. The problem is that these records contain very personal information, so the data must be kept private. |
Keywords
» Artificial intelligence » Federated learning » Machine learning
