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D. Beaver, “Efficient multiparty protocols using circuit randomization,” in Proceedings of the 11th Annual International Cryptology Conference on Advances in Cryptology , ser. CRYPTO ’91. London, UK, UK: Springer-Verlag, 1992, pp. 420–432
1992
Earlier work this paper cites.
T.-S. Chua, J. Tang, R. Hong, H. Li, Z. Luo, and Y.-T. Zheng, “NUS-WIDE: a real-world web image database from national university of singapore,” in Proc. of ACM Conf. on Image and Video Retrieval (CIVR’09) , 2009
2009
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems 25 , F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, Eds., 2012, pp. 1097–1105
2012
Earlier work this paper cites.
I. Damgrd, V. Pastro, N. Smart, and S. Zakarias, “Multiparty Computation from Somewhat Homomorphic Encryption,” in Advances in Cryptology – CRYPTO 2012 , R. Safavi-Naini and R. Canetti, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 643–662
2012
Earlier work this paper cites.
2012
Earlier work this paper cites.
G. Asharov, Y. Lindell, T. Schneider, and M. Zohner, “More efficient oblivious transfer and extensions for faster secure computation,” in Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security , ser. CCS ’13. New York, NY, USA: ACM, 2013, pp. 535–548. [Online]. Available: http://doi.acm.org/10.1145/2508859.2516738
2013
Earlier work this paper cites.
I. Damgrd, M. Keller, E. Larraia, V. Pastro, P. Scholl, and N. P. Smart, “Practical Covertly Secure MPC for Dishonest Majority – Or: Breaking the SPDZ Limits,” in Computer Security – ESORICS 2013 , J. Crampton, S. Jajodia, and K. Mayes, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 1–18
2013
Earlier work this paper cites.
D. Demmler, T. Schneider, and M. Zohner, “ABY - A Framework for Efficient Mixed-Protocol Secure Two-Party Computation,” in NDSS , 2015
2015
Earlier work this paper cites.
X. Shu, G.-J. Qi, J. Tang, and J. Wang, “Weakly-shared deep transfer networks for heterogeneous-domain knowledge propagation,” 10 2015, pp. 35–44
2015
Cited alongside, same era.
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of go with deep neural networks and tree search,” Nature , vol. 529, pp. 484–503, 2016, http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html
2016
Cited alongside, same era.
2016
Cited alongside, same era.
A. Gascón, P. Schoppmann, B. Balle, M. Raykova, J. Doerner, S. Zahur, and D. Evans, “Secure linear regression on vertically partitioned datasets,” IACR Cryptology ePrint Archive , vol. 2016, p. 892, 2016
2018
Later among the works it cites.
2018
Later among the works it cites.
M. Keller, V. Pastro, and D. Rotaru, “Overdrive: Making SPDZ Great Again,” in Advances in Cryptology – EUROCRYPT 2018 , J. B. Nielsen and V. Rijmen, Eds. Cham: Springer International Publishing, 2018, pp. 158–189
2018
Later among the works it cites.
R. Cramer, I. Damgrd, D. Escudero, P. Scholl, and C. Xing, “SPDZ2k: efficient MPC mod 2k for dishonest majority,” in Advances in Cryptology - CRYPTO 2018 - 38th Annual International Cryptology Conference, Santa Barbara, CA, USA, August 19-23, 2018, Proceedings, Part II , 2018, pp. 769–798
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2016
Cited alongside, same era.
R. Gilad-Bachrach, N. Dowlin, K. Laine, K. Lauter, M. Naehrig, , and J. Wernsing, “CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy,” in Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48 , ser. ICML’16. JMLR.org, 2016, pp. 201–210
2016
Cited alongside, same era.
P. Mohassel and Y. Zhang, “SecureML: A System for Scalable Privacy-Preserving Machine Learning,” 2017 IEEE Symposium on Security and Privacy (SP) , pp. 19–38, May 2017
2017
Cited alongside, same era.
R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership inference attacks against machine learning models,” in 2017 IEEE Symposium on Security and Privacy (SP) , May 2017, pp. 3–18
2017
Cited alongside, same era.
A. Segal, A. Marcedone, B. Kreuter, D. Ramage, H. B. McMahan, K. Seth, K. Bonawitz, S. Patel, and V. Ivanov, “Practical secure aggregation for privacy-preserving machine learning,” in CCS , 2017
2017
Cited alongside, same era.
N1 Analytics. MP-SPDZ - Versatile framework for multi-party computation. https://github.com/n1analytics/MP-SPDZ
Cited in the paper.
encryptogroup. ABY - A Framework for Efficient Mixed-protocol Secure Two-party Computation. https://github.com/encryptogroup/ABY
Cited in the paper.
WeBank. FATE - Federated AI Technology Enabler. https://github.com/WeBankFinTech/FATE
Cited in the paper.
2018
Later among the works it cites.
W. Zheng, R. A. Popa, J. E. Gonzalez, and I. Stoica, “Helen: Maliciously Secure Coopetitive Learning for Linear Models,” 2019
2019
Closest in time.
V. Chen, V. Pastro, and M. Raykova, “Secure Computation for Machine Learning With SPDZ,” 2019
2019
Closest in time.
Kaggle. (2019) Default of credit card clients dataset. https://www.kaggle.com/uciml/default-of-credit-card-clients-dataset
2019
Closest in time.