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Sorting Networks and Their Applications
Batcher, K. E. (1968) · 1968
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On data banks and privacy homomorphisms
Rivest, R. L., Adleman, L., and Dertouzos, M. L. (1978) · 1978
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Credit card fraud detection with a neural-network
Ghosh, S. and Reilly, D. L. (1994) · 1994
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Machine learning for medical diagnosis: history, state of the art and perspective
Kononenko, I. (2001) · 2001
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Fully homomorphic encryption using ideal lattices
Gentry, C. (2009) · 2009
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Community detection in graphs
Fortunato, S. (2010) · 2010
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Fully homomorphic encryption with polylog overhead
Gentry, C., Halevi, S., and Smart, N. P. (2012) · 2012
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Homomorphic encryption from learning with errors: Conceptually-simpler, asymptotically-faster, attribute-based
Gentry, C., Sahai, A., and Waters, B. (2013) · 2013
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Regularization of neural networks using dropconnect
Wan, L., Zeiler, M., Zhang, S., Cun, Y. L., and Fergus, R. (2013) · 2013
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Efficient fully homomorphic encryption from (standard) lwe
Brakerski, Z. and Vaikuntanathan, V. (2014) · 2014
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Fully homomorphic simd operations
Smart, N. P. and Vercauteren, F. (2014) · 2014
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Compressing neural networks with the hashing trick
Chen, W., Wilson, J., Tyree, S., Weinberger, K., and Chen, Y. (2015) · 2015
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Binaryconnect: Training deep neural networks with binary weights during propagations
Courbariaux, M., Bengio, Y., and David, J.-P. (2015) · 2015
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Fhew: bootstrapping homomorphic encryption in less than a second
Ducas, L. and Micciancio, D. (2015) · 2015
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Bootstrapping for helib
Halevi, S. and Shoup, V. (2015) · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C. (2015) · 2015
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Bitwise neural networks
Kim, M. and Smaragdis, P. (2015) · 2015
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Faster fully homomorphic encryption: Bootstrapping in less than 0.1 seconds
Chillotti, I., Gama, N., Georgieva, M., and Izabachene, M. (2016) · 2016
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Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1
Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., and Bengio, Y. (2016) · 2016
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Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy
Gilad-Bachrach, R., Dowlin, N., Laine, K., Lauter, K., Naehrig, M., and Wernsing, J. (2016) · 2016
Privacy-preserving classification on deep neural network
Chabanne, H., de Wargny, A., Milgram, J., Morel, C., and Prouff, E. (2017) · 2017
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Private collaborative neural network learning
Chase, M., Gilad-Bachrach, R., Laine, K., Lauter, K., and Rindal, P. (2017) · 2017
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Fxpnet: Training a deep convolutional neural network in fixed-point representation
Chen, X., Hu, X., Zhou, H., and Xu, N. (2017) · 2017
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Homomorphic encryption
Halevi, S. (2017) · 2017
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Hardy, S., Henecka, W., Ivey-Law, H., Nock, R., Patrini, G., Smith, G., and Thorne, B. (2017) · 2017
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Densely connected convolutional networks
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Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding
Han, S., Mao, H., and Dally, W. J. (2016) · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
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Quantized neural networks: Training neural networks with low precision weights and activations
Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., and Bengio, Y. (2016) · 2016
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Zhu, C., Han, S., Mao, H., and Dally, W. J. (2016) · 2016
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Privacy-preserving deep learning via additively homomorphic encryption
Aono, Y., Hayashi, T., Wang, L., Moriai, S., et al · 2017
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Early identification of patients with acute decompensated heart failure
Blecker, S., Sontag, D., Horwitz, L. I., Kuperman, G., Park, H., Reyentovich, A., and Katz, S. D. (2017) · 2017
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Huang, G., Liu, Z., Weinberger, K. Q., and van der Maaten, L. (2017) · 2017
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Oblivious neural network predictions via minionn transformations
Liu, J., Juuti, M., Lu, Y., and Asokan, N. (2017) · 2017
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Secureml: A system for scalable privacy-preserving machine learning
Mohassel, P. and Zhang, Y. (2017) · 2017
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Chameleon: A hybrid secure computation framework for machine learning applications
Riazi, M. S., Weinert, C., Tkachenko, O., Songhori, E. M., Schneider, T., and Koushanfar, F. (2017) · 2017
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Deepsecure: Scalable provably-secure deep learning
Rouhani, B. D., Riazi, M. S., and Koushanfar, F. (2017) · 2017
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TAPAS – tricks for accelerating (encrypted) prediction as a service
(2018) · 2018
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Gazelle: A low latency framework for secure neural network inference
Juvekar, C., Vaikuntanathan, V., and Chandrakasan, A. (2018) · 2018
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Deep learning inferences with hybrid homomorphic encryption
Meehan, A., Ko, R. K. L., and Holmes, G. (2018) · 2018
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