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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Electrical Engineering and Computer (JEECOM) Dirasah : Jurnal Pemikiran dan Pendidikan Dasar Islam
Afdhaluzzikri, Afdhaluzzikri
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Religion Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Languange, Linguistic, Communication & Media Mathematics Other

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Journal : Journal of Electrical Engineering and Computer (JEECOM)

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Enhancing Medical Data Privacy: Neural Network Inference with Fully Homomorphic Encryption Maulyanda, Maulyanda; Deviani, Rini; Afdhaluzzikri, Afdhaluzzikri
Journal of Electrical Engineering and Computer (JEECOM) Vol 7, No 1 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v7i1.10875

Abstract

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.
Co-Authors Athailah, Farhan Deviani, Rini Fathira, Elia Herman Mawengkang Maulyanda, Maulyanda Opim Salim Sitompul Saiful Bahri