BUNET: Blind Medical Image Segmentation Based on Secure UNET

• URL: http://arxiv.org/abs/2007.06855v1
• Date: Tue, 14 Jul 2020 07:05:23 GMT
• Title: BUNET: Blind Medical Image Segmentation Based on Secure UNET
• Authors: Song Bian, Xiaowei Xu, Weiwen Jiang, Yiyu Shi, Takashi Sato
• Abstract summary: 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. We 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.
• Score: 24.374253627122467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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