Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Beaver, D.: Efficient multiparty protocols using circuit randomization. In: Feigenbaum, J. (ed.) CRYPTO 1991. LNCS, vol. 576, pp. 420–432. Springer, Heidelberg (1992). https://doi.org/10.1007/3-540-46766-1_34
Bian, S., Jiang, W., Lu, Q., Shi, Y., Sato, T.: NASS: Optimizing secure inference via neural architecture search. arXiv preprint arXiv:2001.11854 (2020)
Bian, S., Wang, T., Hiromoto, M., Shi, Y., Sato, T.: ENSEI: Efficient secure inference via frequency-domain homomorphic convolution for privacy-preserving visual recognition (2020)
Brakerski, Z.: Fully homomorphic encryption without modulus switching from classical GapSVP. In: Safavi-Naini, R., Canetti, R. (eds.) CRYPTO 2012. LNCS, vol. 7417, pp. 868–886. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32009-5_50
Brakerski, Z., Gentry, C., Vaikuntanathan, V.: (Leveled) fully homomorphic encryption without bootstrapping. ACM Trans. Comput. Theory (TOCT) 6(3), 13 (2014)
Brutzkus, A., Gilad-Bachrach, R., Elisha, O.: Low latency privacy preserving inference. In: International Conference on Machine Learning, pp. 812–821 (2019)
Cardona, A., et al.: An integrated micro-and macroarchitectural analysis of the drosophila brain by computer-assisted serial section electron microscopy. PLoS Biol. 8(10), e1000502 (2010)
Cheon, J.H., Kim, A., Kim, M., Song, Y.: Homomorphic encryption for arithmetic of approximate numbers. In: Takagi, T., Peyrin, T. (eds.) ASIACRYPT 2017. LNCS, vol. 10624, pp. 409–437. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70694-8_15
Damgård, I., Nielsen, J.B., Polychroniadou, A., Raskin, M.: On the communication required for unconditionally secure multiplication. In: Robshaw, M., Katz, J. (eds.) CRYPTO 2016. LNCS, vol. 9815, pp. 459–488. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-53008-5_16
Drolet, B.C., Marwaha, J.S., Hyatt, B., Blazar, P.E., Lifchez, S.D.: Electronic communication of protected health information: privacy, security, and hipaa compliance. J. Hand Surg. 42(6), 411–416 (2017)
Fan, J., Vercauteren, F.: Somewhat practical fully homomorphic encryption. IACR Cryptology ePrint Archive 2012, 144 (2012)
HHS.gov (2009). https://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations/index.html. Accessed 04 Mar 2020
HHS.gov (2009). https://www.hhs.gov/hipaa/for-professionals/privacy/guidance/minimum-necessary-requirement/index.html. Accessed 03 Apr 2020
Hoffman, S., Podgurski, A.: Securing the hipaa security rule. J. Internet Law, Spring, 06–26 (2007)
Juvekar, C., et al.: Gazelle: a low latency framework for secure neural network inference. arXiv preprint arXiv:1801.05507 (2018)
Keller, M., Pastro, V., Rotaru, D.: Overdrive: making SPDZ great again. In: Nielsen, J.B., Rijmen, V. (eds.) EUROCRYPT 2018. LNCS, vol. 10822, pp. 158–189. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78372-7_6
Liu, J., et al.: Oblivious neural network predictions via MinioNN transformations. In: Proceedings of ACM SIGSAC Conference on Computer and Communications Security, pp. 619–631. ACM (2017)
Mohassel, P., et al.: Secureml: a system for scalable privacy-preserving machine learning. In: Proceedings of Security and Privacy (SP), pp. 19–38. IEEE (2017)
MP-SPDZ (2018). https://github.com/data61/MP-SPDZ/. Accessed 03 Oct 2020
Pace, D.F., Dalca, A.V., Geva, T., Powell, A.J., Moghari, M.H., Golland, P.: Interactive whole-heart segmentation in congenital heart disease. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 80–88. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_10
Prados, F., Ashburner, J., Blaiotta, C., Brosch, T., Carballido-Gamio, J., Cardoso, M.J., Conrad, B.N., Datta, E., Dávid, G., De Leener, B., et al.: Spinal cord grey matter segmentation challenge. Neuroimage 152, 312–329 (2017)
Riazi, M.S., Samragh, M., Chen, H., Laine, K., Lauter, K.E., Koushanfar, F.: Xonn: Xnor-based oblivious deep neural network inference. IACR Cryptology ePrint Archive 2019, 171 (2019)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Rouhani, B.D., et al.: Deepsecure: Scalable provably-secure deep learning. In: Proceedings of DAC, pp. 1–6. IEEE (2018)
Microsoft SEAL (release 3.3), June 2019. https://github.com/Microsoft/SEAL, microsoft Research, Redmond, WA
Yao, A.C.: Protocols for secure computations. In: 23rd Annual Symposium on Foundations of Computer Science, 1982. SFCS 2008, pp. 160–164. IEEE (1982)
Acknowledgment
This work was partially supported by JSPS KAKENHI Grant No. 20K19799, 20H04156, Edgecortix Inc, the Science and Technology Planning Project of Guangdong Province under Grant No. 2017A070701013, 2017B090904034, 2017B030314109, 2018B090944002, and 2019B020230003, Guangdong peak project under Grant No. DFJH201802, the National Key Research and Development Program under Grant No. 2018YFC1002600, the Natural Science Foundation of Guangdong Province under Grant No. 2018A030313785.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Bian, S., Xu, X., Jiang, W., Shi, Y., Sato, T. (2020). BUNET: Blind Medical Image Segmentation Based on Secure UNET. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_59
Download citation
DOI: https://doi.org/10.1007/978-3-030-59713-9_59
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59712-2
Online ISBN: 978-3-030-59713-9
eBook Packages: Computer ScienceComputer Science (R0)
