Toward Understanding Efficient Privacy-Preserving Homomorphic Comparison-Reference-Cited by-同舟云学术

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Toward Understanding Efficient Privacy-Preserving Homomorphic Comparison

Published:2023 Issue: Volume:11 Page:102189-102206
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Pulido-Gaytan Bernardo¹^ORCID,Tchernykh Andrei¹^ORCID,Leprévost Franck²^ORCID,Bouvry Pascal³^ORCID,Goldman Alfredo⁴^ORCID

Affiliation:

1. Department of Computer Science, CICESE Research Center, Ensenada, Mexico

2. Department of Computer Science, Faculty of Science, Technology and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg

3. Department of Computer Science, Faculty of Science, Technology and Medicine, and SnT, University of Luxembourg, Esch-sur-Alzette, Luxembourg

4. Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10251405.pdf?arnumber=10251405

Reference72 articles.

1. Calibrating Noise to Sensitivity in Private Data Analysis

2. Efficient Homomorphic Comparison Methods with Optimal Complexity

3. How to play ANY mental game

4. A fully homomorphic encryption scheme;gentry,2009

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. Self-learning activation functions to increase accuracy of privacy-preserving Convolutional Neural Networks with homomorphic encryption;PLOS ONE;2024-07-22

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