A Privacy-Preserving Framework for Collaborative Machine Learning with Kernel Methods-Reference-Cited by-同舟云学术

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A Privacy-Preserving Framework for Collaborative Machine Learning with Kernel Methods

Published:2023-11-01 Issue: Volume: Page:82-90
ISSN:
Container-title:2023 5th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)
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Author:

Hannemann Anika¹,Ünal Ali Burak²,Swaminathan Arjhun²,Buchmann Erik¹,Akgün Mete²

Affiliation:

1. Leipzig University Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI),Dept. of Computer Science,Dresden/Leipzig,Germany

2. University of Tübingen Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen,Medical Data Privacy and Privacy-preserving Machine Learning (MDPPML),Germany

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10431557/10431558/10431639.pdf?arnumber=10431639

Reference30 articles.

1. Federated Learning and Differential Privacy for Medical Image Analysis

2. QM—Three-Dimensional Rotations

3. Privacy Preserving Data Classification with Rotation Perturbation

4. Towards Attack-Resilient Geometric Data Perturbation

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