Privacy-Preserving Wavelet Wavelet Neural Network with Fully Homomorphic Encryption. (arXiv:2205.13265v1 [cs.LG])

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 …

arxiv encryption homomorphic encryption network neural network privacy