SHAPER: A General Architecture for Privacy-Preserving Primitives in Secure Machine Learning
DOI:
https://doi.org/10.46586/tches.v2024.i2.819-843Keywords:
Privacy-Preserving Machine Learning, Multi-Party Computation, Additive Homomorphic Encryption, Hardware AcceleratorAbstract
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 exploration, demonstrating a 94x speed-up over CPU clusters on large-scale logistic regression training tasks.
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Copyright (c) 2024 Ziyuan Liang, Qi’ao Jin, Zhiyong Wang, Zhaohui Chen, Zhen Gu, Yanhheng Lu, Fan Zhang
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This work is licensed under a Creative Commons Attribution 4.0 International License.