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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: http://hdl.handle.net/21.11116/0000-0006-DB9F-1
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