  The main aim of Privacy-Preserving Machine Learning (PPML) is to protect the
privacy and provide security to the data used in building Machine Learning
models. There are various techniques in PPML such as Secure Multi-Party
Computation, Differential Privacy, and Homomorphic Encryption (HE). The
techniques are combined with various Machine Learning models and even Deep
Learning Networks to protect the data privacy as well as the identity of the
user. In this paper, we propose a fully homomorphic encrypted wavelet neural
network to protect privacy and at the same time not compromise on the
efficiency of the model. We tested the effectiveness of the proposed method on
seven datasets taken from the finance and healthcare domains. The results show
that our proposed model performs similarly to the unencrypted model.
