BUNET: Blind Medical Image Segmentation Based on Secure UNET

07/14/2020
by   Song Bian, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/11/2020

ENSEI: Efficient Secure Inference via Frequency-Domain Homomorphic Convolution for Privacy-Preserving Visual Recognition

In this work, we propose ENSEI, a secure inference (SI) framework based ...
research
06/22/2022

Multi-party Secure Broad Learning System for Privacy Preserving

Multi-party learning is an indispensable technique for improving the lea...
research
07/31/2023

A hybrid approach for improving U-Net variants in medical image segmentation

Medical image segmentation is vital to the area of medical imaging becau...
research
05/07/2019

Efficient Neural Architecture Search on Low-Dimensional Data for OCT Image Segmentation

Typically, deep learning architectures are handcrafted for their respect...
research
05/27/2023

Secure and Privacy-preserving Network Slicing in 3GPP 5G System Architecture

Network slicing in 3GPP 5G system architecture has introduced significan...
research
03/15/2023

vFHE: Verifiable Fully Homomorphic Encryption with Blind Hash

Fully homomorphic encryption (FHE) is a powerful encryption technique th...
research
01/30/2020

NASS: Optimizing Secure Inference via Neural Architecture Search

Due to increasing privacy concerns, neural network (NN) based secure inf...

Please sign up or login with your details

Forgot password? Click here to reset