# Privacy-Preserving Deep Neural Networks with Pixel-based Image Encryption Considering Data Augmentation in the Encrypted Domain

arXiv: https://arxiv.org/abs/1905.01827 · HTML: https://mlsanity.com/paper/1905.01827/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2019.

## Built on by (most cited first, 19 of 19)

- [Diff-Privacy: Diffusion-based Face Privacy Protection](https://mlsanity.com/paper/2309.05330/) (2023)
- [A Protection Method of Trained CNN Model with Secret Key from Unauthorized Access](https://mlsanity.com/paper/2105.14756/) (2021)
- [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)
- [Piracy-Resistant DNN Watermarking by Block-Wise Image Transformation with Secret Key](https://mlsanity.com/paper/2104.04241/) (2021)
- [Hiding Visual Information via Obfuscating Adversarial Perturbations](https://mlsanity.com/paper/2209.15304/) (2022)
- [A Protection Method of Trained CNN Model Using Feature Maps Transformed With Secret Key From Unauthorized Access](https://mlsanity.com/paper/2109.00224/) (2021)
- [Access Control Using Spatially Invariant Permutation of Feature Maps for Semantic Segmentation Models](https://mlsanity.com/paper/2109.01332/) (2021)
- [Syfer: Neural Obfuscation for Private Data Release](https://mlsanity.com/paper/2201.12406/) (2022)
- [Access Control of Semantic Segmentation Models Using Encrypted Feature Maps](https://mlsanity.com/paper/2206.05422/) (2022)
- [Multitask Identity-Aware Image Steganography via Minimax Optimization](https://mlsanity.com/paper/2107.05819/) (2021)
- [Transfer Learning-Based Model Protection With Secret Key](https://mlsanity.com/paper/2103.03525/) (2021)
- [Access Control with Encrypted Feature Maps for Object Detection Models](https://mlsanity.com/paper/2209.14831/) (2022)
- [Synthetic Dataset Generation for Privacy-Preserving Machine Learning](https://mlsanity.com/paper/2210.03205/) (2022)
- [AI-Driven Secure Data Sharing: A Trustworthy and Privacy-Preserving Approach](https://mlsanity.com/paper/2501.15363/) (2025)
- [Generation of Gradient-Preserving Images allowing HOG Feature Extraction](https://mlsanity.com/paper/2104.01350/) (2021)
- [A Lightweight Privacy-Preserving Scheme Using Label-based Pixel Block Mixing for Image Classification in Deep Learning](https://mlsanity.com/paper/2105.08876/) (2021)
- [Edge Deep Learning Model Protection via Neuron Authorization](https://mlsanity.com/paper/2303.12397/) (2023)
- [Attack GAN (AGAN ): A new Security Evaluation Tool for Perceptual Encryption](https://mlsanity.com/paper/2407.06570/) (2024)

## Abstract (arXiv)

We present a novel privacy-preserving scheme for deep neural networks (DNNs) that enables us not to only apply images without visual information to DNNs for both training and testing but to also consider data augmentation in the encrypted domain for the first time. In this paper, a novel pixel-based image encryption method is first proposed for privacy-preserving DNNs. In addition, a novel adaptation network is considered that reduces the influence of image encryption. In an experiment, the proposed method is applied to a well-known network, ResNet-18, for image classification. The experimental results demonstrate that conventional privacy-preserving machine learning methods including the state-of-the-arts cannot be applied to data augmentation in the encrypted domain and that the proposed method outperforms them in terms of classification accuracy.