Accelerating Privacy-Preserving Machine Learning With GeniBatch-Reference-Cited by-同舟云学术

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Accelerating Privacy-Preserving Machine Learning With GeniBatch

Published:2024-04-22 Issue: Volume:13 Page:489-504
ISSN:
Container-title:Proceedings of the Nineteenth European Conference on Computer Systems
language:
Short-container-title:

Author:

Huang Xinyang¹^ORCID,Zhang Junxue¹^ORCID,Cheng Xiaodian²^ORCID,Zhang Hong³^ORCID,Jin Yilun¹^ORCID,Hu Shuihai¹^ORCID,Tian Han¹^ORCID,Chen Kai⁴^ORCID

Affiliation:

1. iSING Lab, Hong Kong University of Science and Technology

2. iSING Lab, Hong Kong University of Science and Technology and University of Waterloo

3. University of Waterloo

4. iSING Lab, Hong Kong University of Science and Technology and USTC

Funder

Hong Kong RGC

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3627703.3629563

Reference72 articles.

1. Fate (federated ai technology enabler). https://fate.fedai.org/, 2019.

2. Fedlearner. https://github.com/bytedance/fedlearner, 2019.

3. Tf encrypted. https://github.com/tf-encrypted/tf-encrypted, 2019.

4. Federatedml (federated machine learning). https://github.com/FederatedAI/FATE/tree/master/python/federatedml, 2019.

5. Microsoft seal. https://github.com/Microsoft/SEAL, 2022.

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2. SoK: Fully Homomorphic Encryption Accelerators;ACM Computing Surveys;2024-07-05

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