  Fully homomorphic encryption (FHE) is one of the prospective tools for
privacypreserving machine learning (PPML), and several PPML models have been
proposed based on various FHE schemes and approaches. Although the FHE schemes
are known as suitable tools to implement PPML models, previous PPML models on
FHE encrypted data are limited to only simple and non-standard types of machine
learning models. These non-standard machine learning models are not proven
efficient and accurate with more practical and advanced datasets. Previous PPML
schemes replace non-arithmetic activation functions with simple arithmetic
functions instead of adopting approximation methods and do not use
bootstrapping, which enables continuous homomorphic evaluations. Thus, they
could not use standard activation functions and could not employ a large number
of layers. The maximum classification accuracy of the existing PPML model with
the FHE for the CIFAR-10 dataset was only 77% until now. In this work, we
firstly implement the standard ResNet-20 model with the RNS-CKKS FHE with
bootstrapping and verify the implemented model with the CIFAR-10 dataset and
the plaintext model parameters. Instead of replacing the non-arithmetic
functions with the simple arithmetic function, we use state-of-the-art
approximation methods to evaluate these non-arithmetic functions, such as the
ReLU, with sufficient precision [1]. Further, for the first time, we use the
bootstrapping technique of the RNS-CKKS scheme in the proposed model, which
enables us to evaluate a deep learning model on the encrypted data. We
numerically verify that the proposed model with the CIFAR-10 dataset shows
98.67% identical results to the original ResNet-20 model with non-encrypted
data. The classification accuracy of the proposed model is 90.67%, which is
pretty close to that of the original ResNet-20 CNN model...
