SecGAN: Honest-Majority Maliciously 3PC Framework for Privacy-Preserving Image Synthesis
Yuting Yang, Lin Liu, Shaojing Fu, Junjie Huang, Yuchuan Luo · 2023
The Generative Adversarial Network (GAN) is capable of generating high-quality images, surpassing earlier generative models by producing fake images. To effectively handle the high computational workload and the large number of parameters involved, outsourcing to cloud servers is a more suitable option. However, utilizing cloud servers for image synthesis also presents the risk of privacy breaches. Additionally, existing privacy-preserving GANs operate on a semi-honest model with two parties, which fails to resist malicious attacks. This paper introduces a novel image synthesis framework that involves three parties, enabling privacy-preserving Deep Convolutional GAN(DCGAN) on the cloud in both semi-honest and malicious scenarios. The secure data reconstruction implemented in this framework detects adversary attacks under the malicious model and aborts computation upon detection. Furthermore, we offer a range of highly efficient and accurate secure computation protocols specifically designed for DCGAN-based image synthesis. Extensive experimental results demonstrate that our secure GAN(SecGAN) can produce image quality comparable to that of the plaintext method.