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

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.

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Genre: Conference Paper
Latex : ESCAPED: {E}fficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare

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 Creators:
Ünal, Ali Burak1, Author
Akgün, Mete1, Author
Pfeifer, Nico2, Author           
Affiliations:
1External Organizations, ou_persistent22              
2Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society, ou_40046              

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Language(s): eng - English
 Dates: 2021
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Uenal_AAAI21
URI: https://ojs.aaai.org/index.php/AAAI/article/view/17199
 Degree: -

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Title: Thirty-Fifth AAAI Conference on Artificial Intelligence
Place of Event: Virtual Conference
Start-/End Date: 2021-02-02 - 2021-02-09

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Title: AAAI Technical Track on Machine Learning IV
  Abbreviation : AAAI 2021
  Subtitle : AAAI-21
  Other : Thirty-Fifth AAAI Conference on Artificial Intelligence Technical Tracks 11
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: Palo Alto, CA : AAAI
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 9988 - 9996 Identifier: ISBN: 978-1-57735-866-4