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This study identifies and proposes techniques to alleviate two key bottlenecks to executing deep neural networks in trusted execution environments (TEEs): page thrashing during the execution of convolutional layers and the decryption of large weight matrices in fully-connected layers.
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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 International Conference on Machine Learning (ICML) , 2016
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N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami, “Practical black-box attacks against machine learning,” in Proceedings of the 2017 ACM on Asia conference on computer and communications security , 2017, pp. 506–519
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T. Lee, Z. Lin, S. Pushp, C. Li, Y. Liu, Y. Lee, F. Xu, C. Xu, L. Zhang, and J. Song, “Occlumency: Privacy-preserving remote deep-learning inference using sgx,” in The 25th Annual International Conference on Mobile Computing and Networking (MobiCom) , 2019
2019
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2020
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2020
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K. Kim, C. H. Kim, J. J. Rhee, X. Yu, H. Chen, D. Tian, and B. Lee, “Vessels: efficient and scalable deep learning prediction on trusted processors,” in Proceedings of the 11th ACM Symposium on Cloud Computing , 2020, pp. 462–476
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2018
Cited alongside, same era.
2018
Cited alongside, same era.
2020
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P. Mishra, R. Lehmkuhl, A. Srinivasan, W. Zheng, and R. A. Popa, “Delphi: A cryptographic inference service for neural networks,” in 29th USENIX Security Symposium (USENIX Security) , 2020
2020
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