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

arXiv: https://arxiv.org/abs/2301.04875 · HTML: https://mlsanity.com/paper/2301.04875/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 18 references that are in the map.
- First posted on arXiv 12 Jan 2023.

## Builds on (most cited first, 11 of 11)

- [ShakeDrop Regularization for Deep Residual Learning](https://mlsanity.com/paper/1802.02375/) (2018)
- [Encryption-then-Compression Systems using Grayscale-based Image Encryption for JPEG Images](https://mlsanity.com/paper/1811.00236/) (2018)
- [Learnable Image Encryption](https://mlsanity.com/paper/1804.00490/) (2018)
- [An Overview of Compressible and Learnable Image Transformation with Secret Key and Its Applications](https://mlsanity.com/paper/2201.11006/) (2022)
- [Block-wise Scrambled Image Recognition Using Adaptation Network](https://mlsanity.com/paper/2001.07761/) (2020)
- [A GAN-Based Image Transformation Scheme for Privacy-Preserving Deep Neural Networks](https://mlsanity.com/paper/2006.01342/) (2020)
- [Privacy-Preserving Image Classification Using Isotropic Network](https://mlsanity.com/paper/2204.07707/) (2022)
- [Image and Model Transformation with Secret Key for Vision Transformer](https://mlsanity.com/paper/2207.05366/) (2022)
- [NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Training](https://mlsanity.com/paper/2106.02484/) (2021)
- [NeuraCrypt is not private](https://mlsanity.com/paper/2108.07256/) (2021)
- [Privacy-Preserving Image Classification Using Vision Transformer](https://mlsanity.com/paper/2205.12041/) (2022)

## Built on by (most cited first, 2 of 2)

- [Efficient Fine-Tuning with Domain Adaptation for Privacy-Preserving Vision Transformer](https://mlsanity.com/paper/2401.05126/) (2024)
- [Domain Adaptation for Efficiently Fine-tuning Vision Transformer with Encrypted Images](https://mlsanity.com/paper/2309.02556/) (2023)

## Abstract (arXiv)

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.