Privacy-Preserving Wavelet Wavelet Neural Network with Fully Homomorphic Encryption

• URL: http://arxiv.org/abs/2205.13265v1
• Date: Thu, 26 May 2022 10:40:31 GMT
• Title: Privacy-Preserving Wavelet Wavelet Neural Network with Fully Homomorphic Encryption
• Authors: Syed Imtiaz Ahamed and Vadlamani Ravi
• Abstract summary: Privacy-Preserving Machine Learning (PPML) aims to protect the privacy and provide security to the data used in building Machine Learning models. 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.
• Score: 5.010425616264462
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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