ML with HE: Privacy Preserving Machine Learning Inferences for Genome Studies
Seyma Selcan Magara, Cafer Yildirim, Ferhat Yaman, B. Dilekoglu, F. R. Tutaş, Erdinç Öztürk, Kamer Kaya, Öznur Taştan, Erkay Savaş · arXiv (Cornell University) · 2021
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.