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We present an approach for generating differentially private synthetic text using large language models (LLMs), via private prediction.
Well-read students learn better: On the importance of pre-training compact models
Iulia Turc, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 1908
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Ellen M. Voorhees and Dawn M. Tice. 2000 · 2000
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Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor. 2006 · 2006
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Kobbi Nissim, Sofya Raskhodnikova, and Adam D. Smith. 2007 · 2007
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On the complexity of differentially private data release: efficient algorithms and hardness results
Cynthia Dwork, Moni Naor, Omer Reingold, Guy N Rothblum, and Salil Vadhan. 2009 · 2009
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
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Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, and Kunal Talwar. 2017 · 2017
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Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising
Borja Balle and Yu-Xiang Wang. 2018 · 2018
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Cynthia Dwork and Vitaly Feldman. 2018 · 2018
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Scalable private learning with PATE
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson. 2018 · 2018
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Practical differentially private top-k selection with pay-what-you-get composition
David Durfee and Ryan M Rogers. 2019 · 2019
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The discrete gaussian for differential privacy
Clément L Canonne, Gautam Kamath, and Thomas Steinke. 2020 · 2020
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Bounding, concentrating, and truncating: Unifying privacy loss composition for data analytics
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Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
Parallel context windows for large language models
Nir Ratner, Yoav Levine, Yonatan Belinkov, Ori Ram, Inbal Magar, Omri Abend, Ehud Karpas, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2023 · 2023
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Synthetic text generation with differential privacy: A simple and practical recipe
Xiang Yue, Huseyin Inan, Xuechen Li, Girish Kumar, Julia McAnallen, Hoda Shajari, Huan Sun, David Levitan, and Robert Sim. 2023 · 2023
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In-context learning with long-context models: An in-depth exploration
Amanda Bertsch, Maor Ivgi, Uri Alon, Jonathan Berant, Matthew R Gormley, and Graham Neubig. 2024 · 2024
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Differentially private next-token prediction of large language models
James Flemings, Meisam Razaviyayn, and Murali Annavaram. 2024 · 2024
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Submix: Practical private prediction for large-scale language models
Antonio Ginart, Laurens van der Maaten, James Zou, and Chuan Guo. 2022 · 2022
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LoRA: Low-rank adaptation of large language models
Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
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Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2022 · 2022
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Differentially private decoding in large language models
Jimit Majmudar, Christophe Dupuy, Charith Peris, Sami Smaili, Rahul Gupta, and Richard Zemel. 2022 · 2022
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Flocks of stochastic parrots: Differentially private prompt learning for large language models
Haonan Duan, Adam Dziedzic, Nicolas Papernot, and Franziska Boenisch. 2023 · 2023
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How to dp-fy ML: A practical guide to machine learning with differential privacy
Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, and Abhradeep Guha Thakurta. 2023 · 2023
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Gemma Team. 2024 · 2024
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DP-OPT: Make large language model your privacy-preserving prompt engineer
Junyuan Hong, Jiachen T. Wang, Chenhui Zhang, Zhangheng LI, Bo Li, and Zhangyang Wang. 2024 · 2024
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Harnessing large-language models to generate private synthetic text
Alexey Kurakin, Natalia Ponomareva, Umar Syed, Liam MacDermed, and Andreas Terzis. 2024 · 2024
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wikipedia-movie-data
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Privacy-preserving in-context learning with differentially private few-shot generation
Xinyu Tang, Richard Shin, Huseyin A Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, and Robert Sim. 2024 · 2024
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Differentially private tabular data synthesis using large language models
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Differentially private synthetic data via foundation model APIs 2: Text
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Privacy-preserving instructions for aligning large language models
Da Yu, Peter Kairouz, Sewoong Oh, and Zheng Xu. 2024 · 2024
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