Privacy-Preserving Wavelet Neural Network with Fully Homomorphic Encryption
This addresses privacy concerns for users in sensitive domains like finance and healthcare, but it is incremental as it combines existing techniques.
The paper tackles the problem of privacy in machine learning by proposing a fully homomorphic encrypted wavelet neural network, achieving results that perform similarly to unencrypted models on seven finance and healthcare datasets.
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.