HT2ML: An efficient hybrid framework for privacy-preserving Machine Learning using HE and TEE-Reference-Cited by-同舟云学术

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HT2ML: An efficient hybrid framework for privacy-preserving Machine Learning using HE and TEE

Published:2023-12 Issue: Volume:135 Page:103509
ISSN:0167-4048
Container-title:Computers & Security
language:en
Short-container-title:Computers & Security

Author:

Wang Qifan^ORCID,Zhou Lei,Bai Jianli,Koh Yun Sing,Cui Shujie,Russello Giovanni

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Law,General Computer Science

Reference59 articles.

1. OBFUSCURO: a commodity obfuscation engine on intel SGX;Ahmad,2019

2. On the concrete hardness of learning with errors;Albrecht;J. Math. Cryptol.,2015

3. Sorting networks and their applications;Batcher,1968

4. Evaluating 2-dnf formulas on ciphertexts;Boneh,2005

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhancing paillier to fully homomorphic encryption with semi-honest TEE;Peer-to-Peer Networking and Applications;2024-07-25

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