TY - GEN
T1 - Privacy-Preserving Medical Image Segmentation via Hybrid Trusted Execution Environment
AU - Bian, Song
AU - Jiang, Weiwen
AU - Sato, Takashi
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/12/5
Y1 - 2021/12/5
N2 - 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×.
AB - 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×.
UR - https://www.scopus.com/pages/publications/85119424291
U2 - 10.1109/DAC18074.2021.9586198
DO - 10.1109/DAC18074.2021.9586198
M3 - 会议稿件
AN - SCOPUS:85119424291
T3 - Proceedings - Design Automation Conference
SP - 1347
EP - 1350
BT - 2021 58th ACM/IEEE Design Automation Conference, DAC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 58th ACM/IEEE Design Automation Conference, DAC 2021
Y2 - 5 December 2021 through 9 December 2021
ER -