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The rapid advancement of large language models (LLMs) has revolutionized natural language processing, enabling applications in diverse domains such as healthcare, finance and education.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 2017
Earlier work this paper cites.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
A survey on homomorphic encryption and its applications
Alican Acar, Hidayet Aksu, A Selcuk Uluagac, and Mauro Conti · 2018
Cited alongside, same era.
Pate-gan: Generating synthetic data with differential privacy guarantees
James Jordon, Jinsung Yoon, and Mihaela van der Schaar · 2018
Cited alongside, same era.
Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
Privacy-preserving classification with secret vector machines
Valentin Hartmann, Konark Modi, Josep M Pujol, and Robert West · 2020
Cited alongside, same era.
Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B Brown, Dawn Song, Ulfar Erlingsson, et al · 2021
Cited alongside, same era.
Privacy-preserving machine learning: Methods, challenges and directions
Privacy-preserving large language models: Chatgpt case study based vision and framework
Imdad Ullah, Najm Hassan, Sukhpal Singh Gill, Basem Suleiman, Tariq Ahamed Ahanger, Zawar Shah, Junaid Qadir, and Salil S Kanhere · 2023
Later among the works it cites.
Local privacy-preserving mechanisms and applications in machine learning
Likun Qin and Tianshuo Qiu · 2024
Closest in time.
Privacymind: Large language models can be contextual privacy protection learners
Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, and Wei Cheng · 2024
Closest in time.
On protecting the data privacy of large language models (llms): A survey
Biwei Yan, Kun Li, Minghui Xu, Yueyan Dong, Yue Zhang, Zhaochun Ren, and Xiuzhen Cheng · 2024
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Runhua Xu, Nathalie Baracaldo, and James Joshi · 2021
Cited alongside, same era.
Ai-driven anonymization: Protecting personal data privacy while leveraging machine learning
Le Yang, Miao Tian, Duan Xin, Qishuo Cheng, and Jiajian Zheng · 2024
Closest in time.
State-of-the-art approaches to enhancing privacy preservation of machine learning datasets: A survey
Chaoyu Zhang · 2024
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