Name
Title | ||
|---|---|---|
Privacy-Preserving Medical Image Segmentation via Hybrid Trusted Execution Environment |
Abstract | ||
|---|---|---|
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 25x. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/DAC18074.2021.9586198 | 2021 58TH ACM/IEEE DESIGN AUTOMATION CONFERENCE (DAC) |
DocType | ISSN | Citations |
Conference | 0738-100X | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Song Bian | 1 | 9 | 7.67 |
| Weiwen Jiang | 2 | 95 | 16.21 |
| Takashi Sato | 3 | 81 | 36.76 |