TY - JOUR
T1 - Deep learning-based medical diagnostic services
T2 - a secure, lightweight, and accurate realization
AU - Liu, Xiaoning
AU - Zheng, Yifeng
AU - Yuan, Xingliang
AU - Yi, Xun
N1 - Funding Information:
This work was supported in part by the Australian Research Council (ARC) Discovery Projects (No. DP200103308, No. DP180103251, and No. DP190102835), by the ARC Linkage Project (No. LP160101766), by the HITSZ Start-up Research Grant (No. BA45001023), by the Guangdong Basic and Applied Basic Research Foundation under Grant No. 2021A1515110027, and by the Shenzhen Science and Technology Program under Grant No. RCBS20210609103056041.
Publisher Copyright:
© 2022 - IOS Press. All rights reserved.
PY - 2022/11/23
Y1 - 2022/11/23
N2 - In this paper, we propose CryptMed, a system framework that enables medical service providers to offer secure, lightweight, and accurate medical diagnostic service to their customers via an execution of neural network inference in the ciphertext domain. CryptMed ensures the privacy of both parties with cryptographic guarantees. Our technical contributions include: 1) presenting a secret sharing based inference protocol that can well cope with the commonly-used linear and non-linear NN layers; 2) devising optimized secure comparison function that can efficiently support comparison-based activation functions in NN architectures; 3) constructing a suite of secure smooth functions built on precise approximation approaches for accurate medical diagnoses. We evaluate CryptMed on 6 neural network architectures across a wide range of non-linear activation functions over two benchmark and four real-world medical datasets. We comprehensively compare our system with prior art in terms of end-to-end service workload and prediction accuracy. Our empirical results demonstrate that CryptMed achieves up to respectively 413 ×, 19 ×, and 43 × bandwidth savings for MNIST, CIFAR-10, and medical applications compared with prior art. For the smooth activation based inference, the best choice of our proposed approximations preserve the precision of original functions, with less than 1.2% accuracy loss and could enhance the precision due to the newly introduced activation function family.
AB - In this paper, we propose CryptMed, a system framework that enables medical service providers to offer secure, lightweight, and accurate medical diagnostic service to their customers via an execution of neural network inference in the ciphertext domain. CryptMed ensures the privacy of both parties with cryptographic guarantees. Our technical contributions include: 1) presenting a secret sharing based inference protocol that can well cope with the commonly-used linear and non-linear NN layers; 2) devising optimized secure comparison function that can efficiently support comparison-based activation functions in NN architectures; 3) constructing a suite of secure smooth functions built on precise approximation approaches for accurate medical diagnoses. We evaluate CryptMed on 6 neural network architectures across a wide range of non-linear activation functions over two benchmark and four real-world medical datasets. We comprehensively compare our system with prior art in terms of end-to-end service workload and prediction accuracy. Our empirical results demonstrate that CryptMed achieves up to respectively 413 ×, 19 ×, and 43 × bandwidth savings for MNIST, CIFAR-10, and medical applications compared with prior art. For the smooth activation based inference, the best choice of our proposed approximations preserve the precision of original functions, with less than 1.2% accuracy loss and could enhance the precision due to the newly introduced activation function family.
KW - neural network inference
KW - privacy-preserving medical service
KW - secret sharing
KW - Secure computation
UR - http://www.scopus.com/inward/record.url?scp=85145776546&partnerID=8YFLogxK
U2 - 10.3233/JCS-210165
DO - 10.3233/JCS-210165
M3 - Article
AN - SCOPUS:85145776546
SN - 0926-227X
VL - 30
SP - 795
EP - 827
JO - Journal of Computer Security
JF - Journal of Computer Security
IS - 6
ER -