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

DOI10.1007/978-3-030-31578-8_27zbMath1437.68154OpenAlexW2980220314MaRDI QIDQ3297645

Nico Pfeifer, Mete Akgün, Ali Burak Ünal

Publication date: 20 July 2020

Published in: Cryptology and Network Security (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-030-31578-8_27

zbMATH Keywords

string kernelrandomized encodingprecision medicineprivacy-preserving machine learningRBF kernel

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Medical applications (general) (92C50) Privacy of data (68P27)

Uses Software

• HElib
• geno2pheno
• Kinship-Privacy

Cites Work

• Support-vector networks
• Computationally private randomizing polynomials and their applications
• Algorithms in HElib
• Garbled Circuits as Randomized Encodings of Functions: a Primer
• How to Garble Arithmetic Circuits
• Cryptography in $NC^0$

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