Differential Privacy Image Generation with Reconstruction Loss and Noise Injection Using an Error Feedback SGD

TL;DR

This paper introduces a novel differential privacy image generation framework using error feedback SGD, reconstruction loss, and noise injection, achieving high-quality synthetic images while maintaining privacy across multiple benchmarks.

Contribution

The work presents a new differential privacy generation method with error feedback SGD, improving image quality and utility under the same privacy budget compared to existing methods.

Findings

Achieves state-of-the-art results on MNIST, Fashion-MNIST, and CelebA.

Generates higher quality images with better utility under differential privacy.

Effective for both grayscale and RGB images.

Abstract

Traditional data masking techniques such as anonymization cannot achieve the expected privacy protection while ensuring data utility for privacy-preserving machine learning. Synthetic data plays an increasingly important role as it generates a large number of training samples and prevents information leakage in real data. The existing methods suffer from the repeating trade-off processes between privacy and utility. We propose a novel framework for differential privacy generation, which employs an Error Feedback Stochastic Gradient Descent(EFSGD) method and introduces a reconstruction loss and noise injection mechanism into the training process. We generate images with higher quality and usability under the same privacy budget as the related work. Extensive experiments demonstrate the effectiveness and generalization of our proposed framework for both grayscale and RGB images. We achieve state-of-the-art results over almost all metrics on three benchmarks: MNIST, Fashion-MNIST, and CelebA.