Aumasson, J.-P., Neves, S., Wilcox-O’Hearn, Z., Winnerlein, C.: BLAKE2: simpler, smaller, fast as MD5. In: Jacobson, M., Locasto, M., Mohassel, P., Safavi-Naini, R. (eds.) ACNS 2013. LNCS, vol. 7954, pp. 119–135. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38980-1_8
Bagdasaryan, E., Shmatikov, V.: Blind backdoors in deep learning models. In: USENIX Security (2021)
Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. Commun. ACM 13(7), 422–426 (1970)
Bojar, O., et al.: Findings of the 2016 conference on machine translation. In: Proceedings of the First Conference on Machine Translation (2016)
Brown, T., et al.: Language models are few-shot learners. In: NeurIPS (2020)
Bubeck, S., et al.: Sparks of Artificial General Intelligence: Early experiments with GPT-4. arXiv preprint arXiv:2303.12712 (2023)
Chen, H., Fu, C., Zhao, J., Koushanfar, F.: ProFlip: targeted trojan attack with progressive bit flips. In: IEEE/CVF ICCV (2021)
Chen, X., Liu, C., Li, B., Lu, K., Song, D.: Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning. arXiv preprint arXiv:1712.05526 (2017)
Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: ICML (2008)
Costan, V., Devadas, S.: Intel SGX explained. Cryptology ePrint Archive (2016)
El Merabet, H., Hajraoui, A.: A survey of malware detection techniques based on machine learning. IJACSA (2019)
Fan, B., Andersen, D.G., Kaminsky, M., Mitzenmacher, M.D.: Cuckoo filter: practically better than bloom. In: CoNEXT (2014)
Gallagher, P., Director, A.: Secure Hash Standard (SHS). FIPS PUB (1995)
Goldblum, M., et al.: Dataset security for machine learning: data poisoning, backdoor attacks, and defenses. IEEE PAMI 45(2), 1563–1580 (2022)
Graves, A., Mohamed, A.R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: ICASSP (2013)
Guo, D., Liu, Y., Li, X., Yang, P.: False negative problem of counting bloom filter. IEEE Trans. Knowl. Data Eng. 22(5), 651–664 (2010)
Hilal, W., Gadsden, S.A., Yawney, J.: Financial fraud: a review of anomaly detection techniques and recent advances. Expert Syst. Appl. 193, 116429 (2022)
International Organization for Standardization: Information processing — Use of longitudinal parity to detect errors in information messages. ISO Standard ISO 1155, ISO (2001)
Jagielski, M., Oprea, A., Biggio, B., Liu, C., Nita-Rotaru, C., Li, B.: Manipulating machine learning: poisoning attacks and countermeasures for regression learning. In: IEEE S &P (2018)
Jang, I., Tang, A., Kim, T., Sethumadhavan, S., Huh, J.: Heterogeneous isolated execution for commodity GPUs. In: ASPLOS (2019)
Kinney, S.L.: Trusted Platform Module Basics: Using TPM in Embedded Systems. Elsevier (2006)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NeurIPS (2017)
Le Quoc, D., Gregor, F., Singh, J., Fetzer, C.: SGX-PySpark: secure distributed data analytics. In: WWW (2019)
Mechanics, M.: What runs ChatGPT? Inside Microsoft’s AI supercomputer | Featuring Mark Russinovich (2023). https://youtu.be/Rk3nTUfRZmo
Microsoft Research: Turing NLG: A 17 Billion Parameter Language Model by Microsoft (2021). https://www.microsoft.com/en-us/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft/
Mutlu, O., Kim, J.S.: RowHammer: a retrospective. IEEE TCAD 39(8), 1555–1571 (2020)
Narayanan, D., et al.: PipeDream: generalized pipeline parallelism for DNN training. In: ACM SOSP (2019)
Nvidia: A100 GPU (2023). https://www.nvidia.com/en-us/data-center/a100/
Nvidia: DGX Systems (2023). https://www.nvidia.com/de-de/data-center/dgx-systems/
OpenAI: Chatgpt (2023). https://openai.com/research/chatgpt
OpenAI: GPT-4 Technical Report. arXiv preprint arXiv:2303.08774 (2023)
Orenbach, M., Lifshits, P., Minkin, M., Silberstein, M.: Eleos: ExitLess OS services for SGX enclaves. In: EuroSys (2017)
Ozga, W., Quoc, D.L., Fetzer, C.: Perun: Secure Multi-Stakeholder Machine Learning Framework with GPU Support. arXiv preprint arXiv:2103.16898 (2021)
Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: NeurIPS (2019)
Paudice, A., Muñoz-González, L., Gyorgy, A., Lupu, E.C.: Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection. arXiv preprint arXiv:1802.03041 (2018)
Peri, N., et al.: Deep k-NN defense against clean-label data poisoning attacks. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12535, pp. 55–70. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-66415-2_4
Peterson, W.W., Brown, D.T.: Cyclic codes for error detection. In: Proceedings of the IRE (1961)
Post, M.: A call for clarity in reporting BLEU scores. In: Proceedings of the Third Conference on Machine Translation: Research Papers (2018)
Quoc, D.L., Gregor, F., Arnautov, S., Kunkel, R., Bhatotia, P., Fetzer, C.: SecureTF: a secure TensorFlow framework. In: ACM/IFIP Middleware (2020)
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al.: Improving language understanding by generative pre-training. In: OpenAI (2018)
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI Blog (2019)
Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. JMLR 21(1), 5485–5551 (2020)
Rajbhandari, S., et al.: DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale. arXiv preprint arXiv:2201.05596 (2022)
Rajbhandari, S., Rasley, J., Ruwase, O., He, Y.: ZeRO: memory optimizations toward training trillion parameter models. In: SC 2020 (2020)
Rajbhandari, S., Ruwase, O., Rasley, J., Smith, S., He, Y.: ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning. arXiv preprint arXiv:2104.07857 (2021)
Rakin, A.S., He, Z., Fan, D.: TBT: targeted neural network attack with bit trojan. In: IEEE/CVF CVPR (2020)
Rakin, A.S., He, Z., Li, J., Yao, F., Chakrabarti, C., Fan, D.: T-BFA: targeted bit-flip adversarial weight attack. IEEE PAMI 44(11), 7928–7939 (2022)
Rasley, J., Rajbhandari, S., Ruwase, O., He, Y.: DeepSpeed: system optimizations enable training deep learning models with over 100 billion parameters. In: SIGKDD (2020)
Ren, J., et al.: ZeRO-offload: democratizing billion-scale model training. In: USENIX ATC (2021)
Rivest, R.: The MD5 Message-Digest Algorithm. IETF (1992)
Saha, A., Subramanya, A., Pirsiavash, H.: Hidden trigger backdoor attacks. In: AAAI (2020)
Sarwate, D.V.: Computation of cyclic redundancy checks via table look-up. Commun. ACM 31(8), 1008–1013 (1988)
Shoeybi, M., Patwary, M., Puri, R., LeGresley, P., Casper, J., Catanzaro, B.: Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism. arXiv preprint arXiv:1909.08053 (2020)
Smith, S., et al.: Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model (2022)
Tian, Z., Cui, L., Liang, J., Yu, S.: A comprehensive survey on poisoning attacks and countermeasures in machine learning. ACM Comput. Surv. 55(8), 1–35 (2022)
Tian, Z., Cui, L., Liang, J., Yu, S.: A comprehensive survey on poisoning attacks and countermeasures in machine learning. ACM CSUR (2022)
Tramèr, F., Boneh, D.: Slalom: fast, verifiable and private execution of neural networks in trusted hardware. In: ICLR (2018)
Tsai, C.C., Porter, D.E., Vij, M.: Graphene-SGX: a practical library OS for unmodified applications on SGX. In: USENIX ATC (2017)
Volos, S., Vaswani, K., Bruno, R.: Graviton: trusted execution environments on GPUs. In: USENIX OSDI (2018)
Xia, G., Chen, J., Yu, C., Ma, J.: Poisoning attacks in federated learning: a survey. IEEE Access 11, 10708–10722 (2023)
Xiao, H., Biggio, B., Brown, G., Fumera, G., Eckert, C., Roli, F.: Is feature selection secure against training data poisoning? PMLR (2015)
Yang, C., Wu, Q., Li, H., Chen, Y.: Generative Poisoning Attack Method Against Neural Networks. arXiv preprint arXiv:1703.01340 (2017)
Yao, F., Rakin, A.S., Fan, D.: DeepHammer: depleting the intelligence of deep neural networks through targeted chain of bit flips. In: USENIX Security (2020)
Zhu, J., et al.: Enabling rack-scale confidential computing using heterogeneous trusted execution environment. IEEE S&P (2020)
