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.

中文翻译：

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BUNET：基于安全UNET的盲医学图像分割

各种隐私法规对病历的严格安全要求成为大数据时代的主要障碍。为了确保高效的机器学习即服务方案，同时保护数据机密性，在这项工作中，我们提出了盲 UNET（BUNET），这是一种基于 UNET 架构实现隐私保护医学图像分割的安全协议。在 BUNET 中，我们有效地利用了加密原语，例如同态加密和乱码电路 (GC)，为 UNET 神经架构设计了一个完整的安全协议。此外，我们进行了广泛的架构搜索，以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。在实验中，我们彻底检查了我们协议的参数空间，