[21] Amir Shahroudy, Jun Liu, Tian-Tsong Ng, and Gang Wang. Ntu rgb+d: A large scale dataset for 3d human activity analysis. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1010-1019, 2016.
[22] Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang. Deep high-resolution representation learning for human pose estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5693-5703, 2019.
[23] Kaiming He, Georgia Gkioxari, Piotr Dólar, and Ross Girshick. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 2961-2969, 2017.
[24] Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh. Realtime multi-person 2d pose estimation using part affinity fields. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7291–7299, 2017.
[25] Kim Laine. Simple encrypted arithmetic library (seal) manual, 2017.
[26] Melissa Chase, Hao Chen, Jintai Ding, Shafi Goldwasser, Sergey Gorbunov, Jeffrey Hoffstein, Kristin Lauter, Satya Lokam, Dustin Moody, Travis Morrison, et al. Security of homomorphic encryption. Homomorphic Encryption. org, Redmond WA, Tech. Rep, 2017.
[27] Craig Gentry. A fully homomorphic encryption scheme. Stanford university, 2009.
[28] Ilaria Chillotti, Nicolas Gama, Mariya Georgieva, and Malika Izabachène. Tfhe: fast fully homomorphic encryption over the torus. Journal of Cryptology, 33(1):34-91, 2020.
[29] Pratyush Mishra, Ryan Lehmkuhl, Akshayaram Srinivasan, Wenting Zheng, and Raluca Ada Popa. Delphi: A cryptographic inference service for neural networks. In 29th USENIX Security Symposium (USENIX Security 20), pages 2505-2522, 2020.
[30] Zahra Ghodsi, Akshaj Kumar Veldanda, Brandon Reagen, and Siddharth Garg. Cryptonas: Private inference on a relu budget. Advances in Neural Information Processing Systems, 33:16961-16971, 2020.
[31] Brian Knott, Shobha Venkataraman, Awni Hannun, Shubho Sengupta, Mark Ibrahim, and Laurens van der Maaten. Crypten: Secure multi-party computation meets machine learning. Advances in Neural Information Processing Systems, 34:4961-4973, 2021.
[32] M Sadegh Riazi, Mohammad Samragh, Hao Chen, Kim Laine, Kristin Lauter, and Farinaz Koushanfar. {XONN}:\{XNOR-based\} oblivious deep neural network inference. In 28th USENIX Security Symposium (USENIX Security 19), pages 1501-1518, 2019.
[33] Minsu Cho, Zahra Ghodsi, Brandon Reagen, Siddharth Garg, and Chinmay Hegde. Sphynx: Relu-efficient network design for private inference. arXiv preprint arXiv:2106.11755, 2021.
[34] Qian Lou, Yilin Shen, Hongxia Jin, and Lei Jiang. Safenet: A secure, accurate and fast neural network inference. In International Conference on Learning Representations, 2020.
[35] Minsu Cho, Ameya Joshi, Brandon Reagen, Siddharth Garg, and Chinmay Hegde. Selective network linearization for efficient private inference. In International Conference on Machine Learning, pages 3947-3961. PMLR, 2022.
[36] Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg, and Brandon Reagen. Deepreduce: Relu reduction for fast private inference. In International Conference on Machine Learning, pages 4839-4849. PMLR, 2021.
[37] Roshan Dathathri, Olli Saarikivi, Hao Chen, Kim Laine, Kristin Lauter, Saeed Maleki, Madanlal Musuvathi, and Todd Mytkowicz. Chet: an optimizing compiler for fully-homomorphic neural-network inherencing. In Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, pages 142-156, 2019.
[38] Miran Kim, Xiaogian Jiang, Kristin Lauter, Elkhan Ismayilzada, and Shayan Shams. Secure human action recognition by encrypted neural network inference. Nature communications, 13(1):1-13, 2022.
[39] Miran Kim, Xiaogian Jiang, Kristin Lauter, Elkhan Ismayilzada, and Shayan Shams. Hear: Human action recognition via neural networks on homomorphically encrypted data. arXiv preprint arXiv:2104.09164, 2021.
[40] Hongwu Peng, Shaoyi Huang, Tong Zhou, Yukui Luo, Chenghong Wang, Zigeng Wang, Jiahui Zhao, Xi Xie, Ang Li, Tony Geng, et al. Autorep: Automatic relu replacement for fast private network inference. arXiv preprint arXiv:2308.10134, 2023.