论文标题
Bunet：基于安全的UNET的盲医医学图像细分
BUNET: Blind Medical Image Segmentation Based on Secure UNET
论文作者
论文摘要
各种隐私法规对医疗记录的严格安全要求成为大数据时代的主要障碍。为了在保护数据机密性的同时确保有效的机器学习作为服务方案，我们提出了Blind UNET（BUNET），该协议是一种基于UNET体系结构实现隐私保护的医疗图像细分的安全协议。在Bunet中，我们有效地利用了同构加密和杂交电路（GC）等加密原始图（GC）为UNET神经体系结构设计完整的安全协议。此外，我们在减少具有高维输入数据的基于GC的安全激活协议的计算瓶颈时进行了广泛的体系结构搜索。在实验中，我们彻底检查了协议的参数空间，并表明与在基线体系结构上具有可忽略的准确性降解的状态安全推理技术相比，我们可以达到14倍的推理时间缩短。
The strict security requirements placed on medical records by various privacy regulations become major obstacles in the age of big data. To ensure efficient machine learning as a service schemes while protecting data confidentiality, in this work, we propose blind UNET (BUNET), a secure protocol that implements privacy-preserving medical image segmentation based on the UNET architecture. In BUNET, we efficiently utilize cryptographic primitives such as homomorphic encryption and garbled circuits (GC) to design a complete secure protocol for the UNET neural architecture. In addition, we perform extensive architectural search in reducing the computational bottleneck of GC-based secure activation protocols with high-dimensional input data. In the experiment, we thoroughly examine the parameter space of our protocol, and show that we can achieve up to 14x inference time reduction compared to the-state-of-the-art secure inference technique on a baseline architecture with negligible accuracy degradation.
