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