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We present CrypTFlow, a first of its kind system that converts TensorFlow inference code into Secure Multi-party Computation (MPC) protocols at the push of a button.
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“SecureNN: 3-Party Secure Computation for Neural Network Training,” https://github.com/snwagh/securenn-public , 2019
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016 , 2016, pp. 770–778
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B. Mood, D. Gupta, H. Carter, K. R. B. Butler, and P. Traynor, “Frigate: A Validated, Extensible, and Efficient Compiler and Interpreter for Secure Computation,” in IEEE European Symposium on Security and Privacy, EuroS&P 2016, Saarbrücken, Germany, March 21-24, 2016 , 2016, pp. 112–127
2016
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O. Ohrimenko, F. Schuster, C. Fournet, A. Mehta, S. Nowozin, K. Vaswani, and M. Costa, “Oblivious Multi-Party Machine Learning on Trusted Processors,” in 25th USENIX Security Symposium, USENIX Security 16, Austin, TX, USA, August 10-12, 2016. , 2016, pp. 619–636
2016
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R. Bahmani, M. Barbosa, F. Brasser, B. Portela, A. Sadeghi, G. Scerri, and B. Warinschi, “Secure Multiparty Computation from SGX,” in Financial Cryptography and Data Security - 21st International Conference, FC 2017, Sliema, Malta, April 3-7, 2017, Revised Selected Papers , 2017, pp. 477–497
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H. Corrigan-Gibbs and D. Boneh, “Prio: Private, Robust, and Scalable Computation of Aggregate Statistics,” in 14th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2017, Boston, MA, USA, March 27-29, 2017 , 2017, pp. 259–282
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2017
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P. Mohassel and Y. Zhang, “SecureML: A System for Scalable Privacy-Preserving Machine Learning,” in 2017 IEEE Symposium on Security and Privacy, S&P 2017, San Jose, CA, USA, May 22-26, 2017 , 2017, pp. 19–38
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N. Agrawal, A. S. Shamsabadi, M. J. Kusner, and A. Gascón, “QUOTIENT: Two-Party Secure Neural Network Training and Prediction,” in Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, CCS 2019, London, UK, November 11-15, 2019 , 2019, pp. 1231–1247
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