Privacy-Preserving Medical Image Segmentation via Hybrid Trusted Execution Environment

Recently, it is reported that the-state-of-the-art secure protocol is able to segment a three-dimensional heart CT scan in roughly 3,000 seconds, without revealing any sensitive information related to the parties involved in the computation. In this work, building upon the existing mix-protocol approach, we make use of the trusted execution environment (TEE) to implement a more efficient privacy-preserving medical image segmentation protocol. In the experiment, we show that by offloading the computations of single-party operators to trusted hardware, the latency for a round of privacy-preserving segmentation can be further reduced by 25×.