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In 30th USENIX Security Symposium (USENIX Security 21) (2021)."},{"key":"e_1_3_2_1_5_1","unstructured":"Amazon MPC using enclaves. https:\/\/d1.awsstatic.com\/events\/Summits\/reinvent2022\/CMP403_Enabling-multi-party-analysis-of-sensitive-data-using-AWS-Nitro-Enclaves-.pdf."},{"key":"e_1_3_2_1_6_1","unstructured":"Amazon on device speech recognition. https:\/\/www.amazon.science\/blog\/how-to-make-on-device-speech-recognition-practical."},{"key":"e_1_3_2_1_7_1","unstructured":"AMD SEV. https:\/\/developer.amd.com\/sev\/."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2018.00062"},{"key":"e_1_3_2_1_9_1","unstructured":"Apple app tracking transparency. https:\/\/developer.apple.com\/documentation\/apptrackingtransparency."},{"key":"e_1_3_2_1_10_1","unstructured":"ARM trustzone. https:\/\/www.arm.com\/technologies\/trustzone-for-cortex-a."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-46803-6_12"},{"volume-title":"Paper 2019\/1095","year":"2019","author":"Boyle E.","key":"e_1_3_2_1_12_1","unstructured":"Boyle, E., Gilboa, N., and Ishai, Y. 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Cryptology ePrint Archive, Paper 2013\/081, 2013."},{"key":"e_1_3_2_1_49_1","unstructured":"Intel SGX. https:\/\/www.intel.com\/content\/www\/us\/en\/developer\/tools\/software-guard-extensions\/overview.html."},{"key":"e_1_3_2_1_50_1","unstructured":"Interoperable private attribution. https:\/\/github.com\/patcg-individual-drafts\/ipa\/."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/1007352.1007396"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3579371.3589350"},{"volume-title":"27th USENIX Security Symposium (USENIX Security 18)","year":"2018","author":"Juvekar C.","key":"e_1_3_2_1_53_1","unstructured":"Juvekar, C., Vaikuntanathan, V., and Chandrakasan, A. GAZELLE: A low latency framework for secure neural network inference. In 27th USENIX Security Symposium (USENIX Security 18) (2018)."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3579371.3589053"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3470496.3527415"},{"volume-title":"Neural Information Processing Systems","year":"2021","author":"Knott B.","key":"e_1_3_2_1_56_1","unstructured":"Knott, B., Venkataraman, S., Hannun, A. Y., Sengupta, S., Ibrahim, M., and van der Maaten, L. CrypTen: Secure multi-party computation meets machine learning. In Neural Information Processing Systems (2021)."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP40000.2020.00092"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3445814.3446717"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3470496.3527433"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3470496.3527425"},{"volume-title":"Paper 2022\/368","year":"2022","author":"Menon S. J.","key":"e_1_3_2_1_61_1","unstructured":"Menon, S. J., and Wu, D. J. Spiral: Fast, high-rate single-server PIR via FHE composition. Cryptology ePrint Archive, Paper 2022\/368, 2022."},{"volume-title":"International Conference on Learning Representations","year":"2017","author":"Merity S.","key":"e_1_3_2_1_62_1","unstructured":"Merity, S., Xiong, C., Bradbury, J., and Socher, R. Pointer sentinel mixture models. 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Deep learning recommendation model for personalization and recommendation systems. arXiv:1906.00091."},{"volume-title":"No language left behind: Scaling human-centered machine translation","year":"2022","author":"Team","key":"e_1_3_2_1_73_1","unstructured":"NLLB Team, Costa-juss\u00e0, M. R., Cross, J., \u00c7elebi, O., Elbayad, M., Heafield, K., Heffernan, K., Kalbassi, E., Lam, J., Licht, D., Maillard, J., Sun, A., Wang, S., Wenzek, G., Youngblood, A., Akula, B., Barrault, L., Gonzalez, G. M., Hansanti, P., Hoffman, J., Jarrett, S., Sadagopan, K. R., Rowe, D., Spruit, S., Tran, C., Andrews, P., Ayan, N. F., Bhosale, S., Edunov, S., Fan, A., Gao, C., Goswami, V., Guzm\u00e1n, F., Koehn, P., Mourachko, A., Ropers, C., Saleem, S., Schwenk, H., and Wang, J. No language left behind: Scaling human-centered machine translation, 2022. arXiv:2207.04672."},{"key":"e_1_3_2_1_74_1","unstructured":"NVIDIA cooperative groups. https:\/\/developer.nvidia.com\/blog\/cooperative-groups\/."},{"key":"e_1_3_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1145\/3579371.3589111"},{"key":"e_1_3_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA53966.2022.00034"},{"key":"e_1_3_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA51647.2021.00013"},{"key":"e_1_3_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1145\/2485922.2485971"},{"key":"e_1_3_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1145\/3373376.3378523"},{"volume-title":"Proceedings on Privacy Enhancing Technologies","year":"2020","author":"Ryffel T.","key":"e_1_3_2_1_80_1","unstructured":"Ryffel, T., Tholoniat, P., Pointcheval, D., and Bach, F. R. AriaNN: Low-interaction privacy-preserving deep learning via function secret sharing. Proceedings on Privacy Enhancing Technologies (2020)."},{"key":"e_1_3_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1145\/3470496.3527393"},{"key":"e_1_3_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833702"},{"volume-title":"How to share a secret. Commun. ACM","year":"1979","author":"Shamir A.","key":"e_1_3_2_1_83_1","unstructured":"Shamir, A. How to share a secret. Commun. 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FALCON: Honest-majority maliciously secure framework for private deep learning, 2020. arXiv:2004.02229."},{"key":"e_1_3_2_1_90_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2017.9"},{"key":"e_1_3_2_1_91_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2018.00043"},{"key":"e_1_3_2_1_92_1","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813634"},{"key":"e_1_3_2_1_93_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA53966.2022.00026"},{"key":"e_1_3_2_1_94_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA53966.2022.00026"},{"key":"e_1_3_2_1_95_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA56546.2023.10070984"},{"volume-title":"Proceedings of Machine Learning and Systems 2020","year":"2020","author":"Zhao W.","key":"e_1_3_2_1_96_1","unstructured":"Zhao, W., Xie, D., Jia, R., Qian, Y., Ding, R., Sun, M., and Li, P. Distributed hierarchical GPU parameter server for massive scale deep learning ads systems. 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