ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based 
Machine Learning Algorithms with Applications in Healthcare

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

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ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare

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

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https://ojs.aaai.org/index.php/AAAI/article/view/17199/17006
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Ünal, A. B., Akgün, M., & Pfeifer, N. (2021). ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare. In AAAI Technical Track on Machine Learning IV (pp. 9988-9996). Palo Alto, CA: AAAI. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17199.

Cite as: https://hdl.handle.net/21.11116/0000-0009-3FAC-0

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