Paper 2022/899

Deep Learning-Based Medical Diagnostic Services: A Secure, Lightweight, and Accurate Realization

Xiaoning Liu, RMIT University
Yifeng Zheng, Harbin Institute of Technology, Shenzhen
Xingliang Yuan, Monash University
Xun Yi, RMIT University

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 an 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 $413\times$, $19\times$, and $43\times$ 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.

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Publication info
Published elsewhere. Journal of Computer Security
Secure computation Privacy-preserving medical service Neural network inference Secret sharing
Contact author(s)
xiaoning trust @ gmail com
yifeng zheng @ hit edu cn
xingliang yuan @ monash edu
xun yi @ rmit edu au
2022-07-11: approved
2022-07-09: received
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      author = {Xiaoning Liu and Yifeng Zheng and Xingliang Yuan and Xun Yi},
      title = {Deep Learning-Based Medical Diagnostic Services: A Secure, Lightweight, and Accurate Realization},
      howpublished = {Cryptology ePrint Archive, Paper 2022/899},
      year = {2022},
      note = {\url{}},
      url = {}
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