# A Privacy-Preserving Machine Learning Scheme Using EtC Images

arXiv: https://arxiv.org/abs/2007.08775 · HTML: https://mlsanity.com/paper/2007.08775/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2020.

## Built on by (most cited first, 9 of 9)

- [An Overview of Compressible and Learnable Image Transformation with Secret Key and Its Applications](https://mlsanity.com/paper/2201.11006/) (2022)
- [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)
- [Piracy-Resistant DNN Watermarking by Block-Wise Image Transformation with Secret Key](https://mlsanity.com/paper/2104.04241/) (2021)
- [A Protection Method of Trained CNN Model Using Feature Maps Transformed With Secret Key From Unauthorized Access](https://mlsanity.com/paper/2109.00224/) (2021)
- [Generation of Gradient-Preserving Images allowing HOG Feature Extraction](https://mlsanity.com/paper/2104.01350/) (2021)
- [Protection of SVM Model with Secret Key from Unauthorized Access](https://mlsanity.com/paper/2111.08927/) (2021)
- [A Pixel-based Encryption Method for Privacy-Preserving Deep Learning Models](https://mlsanity.com/paper/2203.16780/) (2022)
- [An Encryption Method of ConvMixer Models without Performance Degradation](https://mlsanity.com/paper/2207.11939/) (2022)

## Abstract (arXiv)

We propose a privacy-preserving machine learning scheme with encryption-then-compression (EtC) images, where EtC images are images encrypted by using a block-based encryption method proposed for EtC systems with JPEG compression. In this paper, a novel property of EtC images is first discussed, although EtC ones was already shown to be compressible as a property. The novel property allows us to directly apply EtC images to machine learning algorithms non-specialized for computing encrypted data. In addition, the proposed scheme is demonstrated to provide no degradation in the performance of some typical machine learning algorithms including the support vector machine algorithm with kernel trick and random forests under the use of z-score normalization. A number of facial recognition experiments with are carried out to confirm the effectiveness of the proposed scheme.