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arXiv · 2110.11446

ML with HE: Privacy Preserving Machine Learning Inferences for Genome Studies

Ş. S. Mağara·C. Yıldırım·F. Yaman·B. Dilekoğlu·F. R. Tutaş·E. Öztürk·K. Kaya·Ö. Taştan·E. Savaş
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Abstract

Preserving the privacy and security of big data in the context of cloud computing, while maintaining a certain level of efficiency of its processing remains to be a subject, open for improvement. One of the most popular applications epitomizing said concerns is found to be useful in genome analysis. This work proposes a secure multi-label tumor classification method using homomorphic encryption, whereby two different machine learning algorithms, SVM and XGBoost, are used to classify the encrypted genome data of different tumor types.

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Ş. S. Mağara, C. Yıldırım, F. Yaman, B. Dilekoğlu, F. R. Tutaş, E. Öztürk, K. Kaya, Ö. Taştan, E. Savaş. 2021-10-21. ML with HE: Privacy Preserving Machine Learning Inferences for Genome Studies. https://arxiv.org/abs/2110.11446

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