  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.
