ZENO: A Type-based Optimization Framework for Zero Knowledge Neural Network Inference
ZENO: A Type-based Optimization Framework for Zero Knowledge Neural Network Inference
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DOI：
10.1145/3617232.3624852
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发表时间：
2024-04
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影响因子：
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通讯作者：
Boyuan Feng;Zheng Wang;Yuke Wang;Shu Yang;Yufei Ding
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文献类型：
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作者：
Boyuan Feng;Zheng Wang;Yuke Wang;Shu Yang;Yufei Ding
零知识神经网络以其简洁的零知识非交互论证(ZkSNARK)安全方案保证神经网络(NNS)的计算完整性和保密性而受到越来越多的关注。然而，zkSNARK神经网络的性能远远不是最优的，这是由于其百万级电路计算具有严重的标量级依赖。本文提出了一种基于类型的优化框架，用于高效的零知识神经网络推理，即ZENO(零知识神经网络优化器)。我们首先引入Zeno语言结构来维护高级语义和类型信息(例如隐私和张量)，以便进行更积极的优化。然后，我们提出了隐私型驱动和张量型驱动的优化方法来进一步优化生成的zkSNARK电路。最后，我们设计了一套以神经网络为中心的系统优化方案，以进一步加速zkSNARK神经网络。实验结果表明，Zeno算法的端到端加速比达到最先进的zkSNARK神经网络算法的8.5倍。我们将VGG16的证明时间从6分钟减少到48秒，这使得zkSNARK NNS具有实用性。
Zero knowledge Neural Networks draw increasing attention for guaranteeing computation integrity and privacy of neural networks (NNs) based on zero-knowledge Succinct Non-interactive ARgument of Knowledge (zkSNARK) security scheme. However, the performance of zkSNARK NNs is far from optimal due to the million-scale circuit computation with heavy scalar-level dependency. In this paper, we propose a type-based optimizing framework for efficient zero-knowledge NN inference, namely ZENO (ZEro knowledge Neural network Optimizer). We first introduce ZENO language construct to maintain high-level semantics and the type information (e.g., privacy and tensor) for allowing more aggressive optimizations. We then propose privacy-type driven and tensor-type driven optimizations to further optimize the generated zkSNARK circuit. Finally, we design a set of NN-centric system optimizations to further accelerate zkSNARK NNs. Experimental results show that ZENO achieves up to 8.5× end-to-end speedup than state-of-the-art zkSNARK NNs. We reduce proof time for VGG16 from 6 minutes to 48 seconds, which makes zkSNARK NNs practical.