A Pixel-based Encryption Method for Privacy-Preserving Deep Learning Models

TL;DR

This paper introduces a new pixel-based encryption technique using XOR and chaotic maps that enhances security while maintaining deep learning classification accuracy on encrypted images.

Contribution

It proposes a novel substitution-based encryption method with chaotic maps and shuffling, improving security without sacrificing deep learning performance.

Findings

Encryption method maintains classification accuracy.

Provides better security than previous pixel-based methods.

Validated on CIFAR datasets with positive results.

Abstract

In the recent years, pixel-based perceptual algorithms have been successfully applied for privacy-preserving deep learning (DL) based applications. However, their security has been broken in subsequent works by demonstrating a chosen-plaintext attack. In this paper, we propose an efficient pixel-based perceptual encryption method. The method provides a necessary level of security while preserving the intrinsic properties of the original image. Thereby, can enable deep learning (DL) applications in the encryption domain. The method is substitution based where pixel values are XORed with a sequence (as opposed to a single value used in the existing methods) generated by a chaotic map. We have used logistic maps for their low computational requirements. In addition, to compensate for any inefficiency because of the logistic maps, we use a second key to shuffle the sequence. We have compared the proposed method in terms of encryption efficiency and classification accuracy of the DL models on them. We have validated the proposed method with CIFAR datasets. The analysis shows that when classification is performed on the cipher images, the model preserves accuracy of the existing methods while provides better security.