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.

翻译：机器学习即服务（MLaaS）允许资源受限的客户端普遍访问强大的数据分析服务。尽管有其优点，但MLaaS在委派计算的完整性以及服务器模型参数的隐私方面带来了重大关切。为解决此问题，Zhang等人（CCS'20）率先开展了零知识机器学习（zkML）的研究。随后提出了一些zkML方案，但它们主要关注单一的机器学习分类算法，这些算法可能无法提供令人满意的精度，或需要大规模的训练数据和模型参数，这对于某些应用来说可能并不理想。我们提出了ezDPS，一种新的高效且零知识的机器学习推理方案。与先前工作不同，ezDPS是一个zkML流水线，其中数据经过多个阶段处理以获得高精度。ezDPS的每个阶段都配备了一种成熟的机器学习算法，这些算法在各种应用中均表现出有效性，包括离散小波变换、主成分分析和支持向量机。我们设计了新的工具来有效证明机器学习操作。我们完整实现了ezDPS，并在真实数据集上评估了其性能。实验结果表明，ezDPS在所有指标上比基于通用电路的方法效率提高了一到三个数量级，同时相比单一机器学习分类方法保持了更理想的精度。