Open AccessESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare
Author(s) -
Ali Burak Ünal,
Mete Akgün,
Nico Pfeifer
Publication year - 2021
Publication title -
proceedings of the aaai conference on artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2374-3468
pISSN - 2159-5399
DOI - 10.1609/aaai.v35i11.17199
Subject(s) - computer science , machine learning , differential privacy , artificial intelligence , kernel (algebra) , algorithm , cluster analysis , data mining , dot product , noise (video) , mathematics , combinatorics , image (mathematics) , geometry
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