[1] Ehud Aharoni, Allon Adir, Moran Baruch, Nir Drucker, Gilad Ezov, Ariel Farkash, Lev Greenberg, Ramy Masalha, Guy Moshkowich, Dov Murik, et al. Helayers: A tile tensors framework for large neural networks on encrypted data. Proceedings on Privacy Enhancing Technologies, 1(1):325-342, 2023.
[2] Fabian Boemer, Anamaria Costache, Rosario Cammarota, and Casimir Wierzynski. ngraph-he2: A high-throughput framework for neural network inference on encrypted data. In Proceedings of the 7th ACM Workshop on Encrypted Computing & Applied Homomorphic Cryptography, pages 45-56, 2019.
[3] Zvika Brakerski, Craig Gentry, and Vinod Vaikuntanathan. (leveled) fully homomorphic encryption without bootstrapping. ACM Transactions on Computation Theory (TOCT), 6(3):1-36, 2014.
[4] Alon Brutzkus, Ran Gilad-Bachrach, and Oren Elisha. Low latency privacy preserving inference. In International Conference on Machine Learning, pages 812-821. PMLR, 2019.
[5] Jung Hee Cheon, Kyoohyung Han, Andrey Kim, Miran Kim, and Yongsoo Song. A full rms variant of approximate homomorphic encryption. In International Conference on Selected Areas in Cryptography, pages 347-368. Springer, 2018.
[6] Jung Hee Cheon, Andrey Kim, Miran Kim, and Yongsoo Song. Homomorphic encryption for arithmetic of approximate numbers. In International Conference on the Theory and Application of Cryptology and Information Security, pages 409-437. Springer, 2017.
[7] Ilaria Chillotti, Nicolas Gama, Mariya Georgieva, and Malika Izabachene. Tfhe: fast fully homomorphic encryption over the torus. Journal of Cryptology, 33(1):34-91, 2020.
[8] 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 inferencing. In Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, pages 142-156, 2019.
[9] Craig Gentry. A fully homomorphic encryption scheme. Stanford university, 2009.
[10] Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, and John Wernsing. Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy. In International conference on machine learning, pages 201-210. PMLR, 2016.
[11] C Lee Giles, Kurt D Bollacker, and Steve Lawrence. Citeseer: An automatic citation indexing system. In Proceedings of the third ACM conference on Digital libraries, pages 89–98, 1998.
[12] Shai Halevi and Victor Shoup. Algorithms in helib. In Annual Cryptology Conference, pages 554-571. Springer, 2014.
[13] Zhicong Huang, Cheng Hong, Wen-jie Lu, Chenkai Weng, and Hunter Qu. More efficient secure matrix multiplication for unbalanced recommender systems. IEEE Transactions on Dependable and Secure Computing, 2021.
[14] Zhicong Huang, Wen-jie Lu, Cheng Hong, and Jiansheng Ding. Cheetah: Lean and fast secure two-party deep neural network inference. IACR Cryptol. ePrint Arch., 2022:207, 2022.
[15] Xiaoqian Jiang, Miran Kim, Kristin Lauter, and Yongsoo Song. Secure outsourced matrix computation and application to neural networks. In Proceedings of the 2018 ACM SIGSAC conference on computer and communications security, pages 1209-1222, 2018.
[16] Wonkyung Jung, Eojin Lee, Sangpyo Kim, Jongmin Kim, Namhoon Kim, Keewoo Lee, Chohong Min, Jung Hee Cheon, and Jung Ho Ahn. Accelerating fully homomorphic encryption through architecture-centric analysis and optimization. IEEE Access, 9:98772-98789, 2021.