A Framework with Randomized Encoding for a Fast Privacy Preserving Calculation 
of Non-linear Kernels for Machine Learning Applications in Precision Medicine

Ünal, Ali Burak; Akgün, Mete; Pfeifer, Nico

doi:10.1007/978-3-030-31578-8_27

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A Framework with Randomized Encoding for a Fast Privacy Preserving Calculation of Non-linear Kernels for Machine Learning Applications in Precision Medicine

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Pfeifer, Nico
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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Ünal, A. B., Akgün, M., & Pfeifer, N. (2019). A Framework with Randomized Encoding for a Fast Privacy Preserving Calculation of Non-linear Kernels for Machine Learning Applications in Precision Medicine. In Y. Mu, R. H. Deng, & X. Huang (Eds.), Cryptology and Network Security (pp. 493-511). Berlin: Springer. doi:10.1007/978-3-030-31578-8_27.

Cite as: https://hdl.handle.net/21.11116/0000-0006-DB9F-1

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