Enhancing Medical Data Privacy: Neural Network Inference with Fully Homomorphic Encryption
Protecting the privacy of medical data while enabling sophisticated data analysis is a critical challenge in modern healthcare. Fully Homomorphic Encryption (FHE) emerges as a powerful solution, enabling computations to be performed directly on encrypted data without exposing sensitive information....
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| Format: | Article |
| Language: | Indonesian |
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LP3M Universitas Nurul Jadid
2025-04-01
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| Series: | Journal of Electrical Engineering and Computer |
| Subjects: | |
| Online Access: | https://ejournal.unuja.ac.id/index.php/jeecom/article/view/10875 |
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| _version_ | 1849716166110478336 |
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| author | Maulyanda Maulyanda Rini Deviani Afdhaluzzikri Afdhaluzzikri |
| author_facet | Maulyanda Maulyanda Rini Deviani Afdhaluzzikri Afdhaluzzikri |
| author_sort | Maulyanda Maulyanda |
| collection | DOAJ |
| description | Protecting the privacy of medical data while enabling sophisticated data analysis is a critical challenge in modern healthcare. Fully Homomorphic Encryption (FHE) emerges as a powerful solution, enabling computations to be performed directly on encrypted data without exposing sensitive information. This study delves into the use of FHE for neural network inference in medical applications, investigating its role in safeguarding patient confidentiality while ensuring computational accuracy and efficiency. Experimental findings confirm the practicality of using FHE for medical data classification, demonstrating that data security can be preserved without significant loss of performance. Furthermore, the research explores the balance between computational overhead and model precision, shedding light on the complexities of deploying FHE in real-world healthcare AI systems. By emphasizing the significance of privacy-preserving machine learning, this work contributes to the development of secure, ethical, and effective AI-driven medical solutions. |
| format | Article |
| id | doaj-art-afcbb84b590a4ea3adf97ed48ad7dad1 |
| institution | DOAJ |
| issn | 2715-0410 2715-6427 |
| language | Indonesian |
| publishDate | 2025-04-01 |
| publisher | LP3M Universitas Nurul Jadid |
| record_format | Article |
| series | Journal of Electrical Engineering and Computer |
| spelling | doaj-art-afcbb84b590a4ea3adf97ed48ad7dad12025-08-20T03:13:07ZindLP3M Universitas Nurul JadidJournal of Electrical Engineering and Computer2715-04102715-64272025-04-017111412410.33650/jeecom.v7i1.108753877Enhancing Medical Data Privacy: Neural Network Inference with Fully Homomorphic EncryptionMaulyanda Maulyanda0Rini DevianiAfdhaluzzikri AfdhaluzzikriUniversitas Syiah KualaProtecting the privacy of medical data while enabling sophisticated data analysis is a critical challenge in modern healthcare. Fully Homomorphic Encryption (FHE) emerges as a powerful solution, enabling computations to be performed directly on encrypted data without exposing sensitive information. This study delves into the use of FHE for neural network inference in medical applications, investigating its role in safeguarding patient confidentiality while ensuring computational accuracy and efficiency. Experimental findings confirm the practicality of using FHE for medical data classification, demonstrating that data security can be preserved without significant loss of performance. Furthermore, the research explores the balance between computational overhead and model precision, shedding light on the complexities of deploying FHE in real-world healthcare AI systems. By emphasizing the significance of privacy-preserving machine learning, this work contributes to the development of secure, ethical, and effective AI-driven medical solutions.https://ejournal.unuja.ac.id/index.php/jeecom/article/view/10875fully homomorphicencryptiondata privacyneural network inference |
| spellingShingle | Maulyanda Maulyanda Rini Deviani Afdhaluzzikri Afdhaluzzikri Enhancing Medical Data Privacy: Neural Network Inference with Fully Homomorphic Encryption Journal of Electrical Engineering and Computer fully homomorphic encryption data privacy neural network inference |
| title | Enhancing Medical Data Privacy: Neural Network Inference with Fully Homomorphic Encryption |
| title_full | Enhancing Medical Data Privacy: Neural Network Inference with Fully Homomorphic Encryption |
| title_fullStr | Enhancing Medical Data Privacy: Neural Network Inference with Fully Homomorphic Encryption |
| title_full_unstemmed | Enhancing Medical Data Privacy: Neural Network Inference with Fully Homomorphic Encryption |
| title_short | Enhancing Medical Data Privacy: Neural Network Inference with Fully Homomorphic Encryption |
| title_sort | enhancing medical data privacy neural network inference with fully homomorphic encryption |
| topic | fully homomorphic encryption data privacy neural network inference |
| url | https://ejournal.unuja.ac.id/index.php/jeecom/article/view/10875 |
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