An Efficient Homomorphic Argmax Approximation for Privacy-Preserving Neural Networks
- DOI
- 10.3390/cryptography8020018
- Published
- 2024-05-01
- Container
- Cryptography
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/cryptography8020018,
title = {An Efficient Homomorphic Argmax Approximation for Privacy-Preserving Neural Networks},
author = {Peng Zhang and Ao Duan and Hengrui Lu},
year = {2024},
journal = {Cryptography},
doi = {10.3390/cryptography8020018},
url = {https://doi.org/10.3390/cryptography8020018}
}RIS
TY - JOUR TI - An Efficient Homomorphic Argmax Approximation for Privacy-Preserving Neural Networks AU - Peng Zhang AU - Ao Duan AU - Hengrui Lu PY - 2024 JO - Cryptography DO - 10.3390/cryptography8020018 UR - https://doi.org/10.3390/cryptography8020018 ER -
APA
Zhang, P., Duan, A., & Lu, H. (2024). An Efficient Homomorphic Argmax Approximation for Privacy-Preserving Neural Networks. Cryptography. https://doi.org/10.3390/cryptography8020018
Source records
- crossref · retrieved 2026-11-10T06:01:53.642Z