论文标题
EZDPS：高效且零知识的机器学习推理管道
ezDPS: An Efficient and Zero-Knowledge Machine Learning Inference Pipeline
论文作者
论文摘要
机器学习作为服务（MLAAS）允许有限的资源客户端访问强大的数据分析服务。尽管有其优点，但MLAA对委派计算的完整性和服务器模型参数的隐私构成了重大关注。为了解决这个问题，Zhang等人。 （CCS'20）启动了零知识机器学习（ZKML）的研究。此后，很少有人提出ZKML方案。但是，他们专注于唯一的ML分类算法，这些算法可能无法提供令人满意的准确性或需要大规模培训数据和模型参数，这对于某些应用而言可能不可取。我们提出了EZDP，这是一种新的高效和零知识的ML推理方案。与先前的工作不同，EZDPS是ZKML管道，其中数据在多个阶段进行处理以高精度。 EZDP的每个阶段都使用已建立的ML算法来利用，该算法在各种应用中有效，包括离散小波转换，主成分分析和支持向量机。我们设计新的小工具以有效地证明ML操作。我们完全实施了EZDP，并评估了其在实际数据集上的性能。实验结果表明，在所有指标中，EZDPS比基于通用电路的方法的一到三个数量级要高，同时比单个ML分类方法保持了更理想的准确性。
Machine Learning as a service (MLaaS) permits resource-limited clients to access powerful data analytics services ubiquitously. Despite its merits, MLaaS poses significant concerns regarding the integrity of delegated computation and the privacy of the server's model parameters. To address this issue, Zhang et al. (CCS'20) initiated the study of zero-knowledge Machine Learning (zkML). Few zkML schemes have been proposed afterward; however, they focus on sole ML classification algorithms that may not offer satisfactory accuracy or require large-scale training data and model parameters, which may not be desirable for some applications. We propose ezDPS, a new efficient and zero-knowledge ML inference scheme. Unlike prior works, ezDPS is a zkML pipeline in which the data is processed in multiple stages for high accuracy. Each stage of ezDPS is harnessed with an established ML algorithm that is shown to be effective in various applications, including Discrete Wavelet Transformation, Principal Components Analysis, and Support Vector Machine. We design new gadgets to prove ML operations effectively. We fully implemented ezDPS and assessed its performance on real datasets. Experimental results showed that ezDPS achieves one-to-three orders of magnitude more efficient than the generic circuit-based approach in all metrics while maintaining more desirable accuracy than single ML classification approaches.
