Privacy-Preserving Wavelet Neural Network with Fully Homomorphic Encryption

TL;DR

This paper introduces a fully homomorphic encryption-based wavelet neural network that maintains high efficiency while ensuring data privacy, tested across finance and healthcare datasets.

Contribution

It presents a novel privacy-preserving wavelet neural network leveraging fully homomorphic encryption, balancing privacy with model performance.

Findings

Model performs comparably to unencrypted versions.

Effective privacy protection demonstrated on real datasets.

Maintains efficiency with encrypted data processing.

Abstract

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.