Abstract
Medical imaging data contain sensitive patient information requiring strong privacy protection. Many analytical setups require data to be sent to a server for inference purposes. Homomorphic encryption (HE) provides a solution by allowing computations to be performed on encrypted data without revealing the original information. However, HE inference is computationally expensive, particularly for large images (e.g., chest X-rays). In this study, we propose an HE inference framework for medical images that uses VQGAN to compress images into latent representations, thereby significantly reducing the computational burden while preserving image quality. We approximate the activation functions with lower-degree polynomials to balance the accuracy and efficiency in compliance with HE requirements. We observed that a downsampling factor of eight for compression achieved an optimal balance between performance and computational cost. We further adapted the squeeze and excitation module, which is known to improve traditional CNNs, to enhance the HE framework. Our method was tested on two chest X-ray datasets for multi-label classification tasks using vanilla CNN backbones. Although HE inference remains relatively slow and introduces minor performance differences compared with unencrypted inference, our approach shows strong potential for practical use in medical images
Abstract (translated)
医学影像数据包含敏感的患者信息，需要强有力的隐私保护措施。许多分析设置要求将数据发送到服务器进行推理操作。同态加密（HE）提供了一种解决方案，在不解密的情况下对加密数据执行计算。然而，同态加密推理在计算上非常昂贵，特别是对于大尺寸图像（例如胸部X光片）。在这项研究中，我们提出了一种针对医学影像的基于VQGAN压缩技术的同态加密推理框架，通过将图像压缩为潜在表示来显著减少计算负担同时保持图像质量。我们将激活函数近似为低阶多项式以平衡准确性和效率，并满足同态加密的要求。观察到在压缩时采用8倍下采样因子可以实现性能与计算成本之间的最佳平衡点。我们进一步改进了挤压和激励模块，这一技术被证明能够提升传统卷积神经网络（CNN）的表现力，从而增强我们的HE框架。我们在两个胸部X光片数据集上使用基本的CNN骨干结构进行了多标签分类任务测试。尽管同态加密推理仍然相对缓慢，并且与未加密推理相比引入了较小的性能差异，但我们的方法在医学影像的实际应用中显示出巨大的潜力。
URL
https://arxiv.org/abs/2506.15258
