SHAPER: A General Architecture for Privacy-Preserving Primitives in Secure Machine Learning
Abstract: Secure multi-party computation and homomorphic encryption are two primary security primitives in privacy-preserving machine learning, whose wide adoption is, nevertheless, constrained by the computation and network communication overheads. This paper proposes a hybrid Secret-sharing and Homomorphic encryption Architecture for Privacy-pERsevering machine learning (SHAPER). SHAPER protects sensitive data in encrypted or randomly shared domains instead of relying on a trusted third party. The proposed algorithm-protocol-hardware co-design methodology explores techniques such as plaintext Single Instruction Multiple Data (SIMD) and fine-grained scheduling, to minimize end-to-end latency in various network settings. SHAPER also supports secure domain computing acceleration and the conversion between mainstream privacy-preserving primitives, making it ready for general and distinctive data characteristics. SHAPER is evaluated by FPGA prototyping with a comprehensive hyper-parameter explo.... https://tches.iacr.org/index.php/TCHES/article/view/11448
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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SHAPER: A General Architecture for Privacy-Preserving Primitives in Secure Machine Learning ; volume:2024 ; number:2 ; year:2024
IACR transactions on cryptographic hardware and embedded systems ; 2024, Heft 2 (2024)
- Creator
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Liang, Ziyuan
Jin, Qi’ao
Wang, Zhiyong
Chen, Zhaohui
Gu, Zhen
Lu, Yanhheng
Zhang, Fan
- DOI
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10.46586/tches.v2024.i2.819-843
- URN
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urn:nbn:de:101:1-2024032017570473024300
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
- 14.08.2025, 10:44 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Liang, Ziyuan
- Jin, Qi’ao
- Wang, Zhiyong
- Chen, Zhaohui
- Gu, Zhen
- Lu, Yanhheng
- Zhang, Fan
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