Color-NeuraCrypt: Privacy-Preserving Color-Image Classification Using Extended Random Neural Networks
This addresses privacy-preserving color-image classification for data owners using cloud computing, but it is incremental as it builds on NeuraCrypt.
The paper tackled the problem of degraded performance in NeuraCrypt for privacy-preserving deep learning when using color images, proposing Color-NeuraCrypt which achieved better classification accuracy than the original and other methods.
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.