用加密图像做医疗诊断，隐私安全且准确
Privacy-Preserving AI for Encrypted Medical Imaging: A Framework for Secure Diagnosis and Learning
- 用改进的卷积网络在加密图像上直接推理
- 准确率和延迟仅略有下降，存储效率高
- 适合需要保护患者隐私的医疗AI场景
人工智能在医疗诊断中的快速应用引发了患者隐私的紧迫担忧，尤其当敏感影像数据需传输、存储或处理时。本文提出一种新型隐私保护诊断推理框架，采用经修改的卷积神经网络（Masked-CNN），可在变换或加密的图像格式上运行。方法结合AES-CBC加密与JPEG2000压缩，在保障图像适合AI推理的同时实现隐私保护。在公开的DICOM数据集（NIH ChestX-ray14和LIDC-IDRI）上评估，重点考察诊断准确率、推理延迟、存储效率及隐私泄露抵抗能力。实验结果表明，加密推理模型性能与未加密版本相当，准确率和延迟仅有微小损失。该框架弥合了数据隐私与临床实用性之间的差距，提供了一种可实际部署、可扩展的隐私安全医疗AI诊断解决方案。
原文摘要 · Abstract (English)
The rapid integration of Artificial Intelligence (AI) into medical diagnostics has raised pressing concerns about patient privacy, especially when sensitive imaging data must be transferred, stored, or processed. In this paper, we propose a novel framework for privacy-preserving diagnostic inference on encrypted medical images using a modified convolutional neural network (Masked-CNN) capable of operating on transformed or ciphered image formats. Our approach leverages AES-CBC encryption coupled with JPEG2000 compression to protect medical images while maintaining their suitability for AI inference. We evaluate the system using public DICOM datasets (NIH ChestX-ray14 and LIDC-IDRI), focusing on diagnostic accuracy, inference latency, storage efficiency, and privacy leakage resistance. Experimental results show that the encrypted inference model achieves performance comparable to its unencrypted counterpart, with only marginal trade-offs in accuracy and latency. The proposed framework bridges the gap between data privacy and clinical utility, offering a practical, scalable solution for secure AI-driven diagnostics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。