Color-NeuraCrypt: Privacy-Preserving Color-Image Classification Using Extended Random Neural Networks

TL;DR

This paper introduces Color-NeuraCrypt, an extension of NeuraCrypt, that enhances privacy-preserving color image classification by improving accuracy over previous methods, suitable for cloud-based deep learning.

Contribution

The paper presents Color-NeuraCrypt, a novel extension that significantly improves privacy-preserving color image classification accuracy compared to existing methods.

Findings

Achieves higher classification accuracy than original NeuraCrypt.

Outperforms other privacy-preserving methods in experiments.

Effective for privacy-preserving color image analysis.

Abstract

In recent years, with the development of cloud computing platforms, privacy-preserving methods for deep learning have become an urgent problem. NeuraCrypt is a private random neural network for privacy-preserving that allows data owners to encrypt the medical data before the data uploading, and data owners can train and then test their models in a cloud server with the encrypted data directly. However, we point out that the performance of NeuraCrypt is heavily degraded when using color images. In this paper, we propose a Color-NeuraCrypt to solve this problem. Experiment results show that our proposed Color-NeuraCrypt can achieve a better classification accuracy than the original one and other privacy-preserving methods.