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

Source: [Color-NeuraCrypt: Privacy-Preserving Color-Image Classification Using Extended Random Neural Networks](<https://www.sota2.com/research/paper/68fa6e581e209131cea3f37f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:55:03.570Z

Authors: Zheng Qi, AprilPyone MaungMaung, Hitoshi Kiya

Published: 2023-01-12T08:47:33.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2301.04875>)

[PDF](<https://arxiv.org/pdf/2301.04875.pdf>)