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We study the problem of in-context learning (ICL) with large language models (LLMs) on private datasets.
Building a question answering test collection
Ellen M. Voorhees and Dawn M. Tice · 2000
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Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2010
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A conversational movie search system based on conditional random fields
Jingjing Liu, Scott Cyphers, Panupong Pasupat, Ian McGraw, and James R. Glass · 2012
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Broadening the scope of differential privacy using metrics
Konstantinos Chatzikokolakis, Miguel E Andrés, Nicolás Emilio Bordenabe, and Catuscia Palamidessi · 2013
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Local privacy and statistical minimax rates
John C. Duchi, Michael I. Jordan, and Martin J. Wainwright · 2013
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Opinion 05/2014 on “Anonymisation Techniques”, 2014
Art. 29 WP · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
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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
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Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Cs7880: Rigorous approaches to data privacy, spring 2017
Jonathan Ullman · 2017
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Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising
Borja Balle and Yu-Xiang Wang · 2018
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Privacy amplification by subsampling: Tight analyses via couplings and divergences
Borja Balle, Gilles Barthe, and Marco Gaboardi · 2018
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Scalable private learning with PATE
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson · 2018
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Privacy-and utility-preserving textual analysis via calibrated multivariate perturbations
Oluwaseyi Feyisetan, Borja Balle, Thomas Drake, and Tom Diethe · 2020
Cited alongside, same era.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi · 2020
Cited alongside, same era.
Permute-and-flip: A new mechanism for differentially private selection
Ryan McKenna and Daniel R Sheldon · 2020
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Official release of source code for the disclosure avoidance system (das) used to protect against the disclosure of individual information based on published statistical summaries, 2020
US Census Bureau · 2020
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A differentially private text perturbation method using regularized mahalanobis metric
Zekun Xu, Abhinav Aggarwal, Oluwaseyi Feyisetan, and Nathanael Teissier · 2020
Cited alongside, same era.
Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer · 2022
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Seqpate: Differentially private text generation via knowledge distillation
Zhiliang Tian, Yingxiu Zhao, Ziyue Huang, Yu-Xiang Wang, Nevin L. Zhang, and He He · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou · 2022
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An explanation of in-context learning as implicit bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma · 2022
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Differentially private fine-tuning of language models
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, and Huishuai Zhang · 2022
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Ricardo Silva Carvalho, Theodore Vasiloudis, and Oluwaseyi Feyisetan · 2021
Cited alongside, same era.
The permute-and-flip mechanism is identical to report-noisy-max with exponential noise
Zeyu Ding, Daniel Kifer, Thomas Steinke, Yuxin Wang, Yingtai Xiao, Danfeng Zhang, et al · 2021
Cited alongside, same era.
Numerical composition of differential privacy
Sivakanth Gopi, Yin Tat Lee, and Lukas Wutschitz · 2021
Cited alongside, same era.
Calibrate before use: Improving few-shot performance of language models
Tony Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh · 2021
Cited alongside, same era.
A critical review on the use (and misuse) of differential privacy in machine learning
Alberto Blanco-Justicia, David Sanchez, Josep Domingo-Ferrer, and Krishnamurty Muralidhar · 2022
Cited alongside, same era.
Submix: Practical private prediction for large-scale language models
Antonio Ginart, Laurens van der Maaten, James Zou, and Chuan Guo · 2022
Cited alongside, same era.
Large language models can be strong differentially private learners
Xuechen Li, Florian Tramèr, Percy Liang, and Tatsunori Hashimoto · 2022
Cited alongside, same era.
Aldo Gael Carranza, Rezsa Farahani, Natalia Ponomareva, Alex Kurakin, Matthew Jagielski, and Milad Nasr · 2023
Closest in time.
A survey on in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, Lei Li, and Zhifang Sui · 2023
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Sanitizing sentence embeddings (and labels) for local differential privacy
Minxin Du, Xiang Yue, Sherman SM Chow, and Huan Sun · 2023
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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
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Privacy-preserving domain adaptation of semantic parsers
Fatemehsadat Mireshghallah, Yu Su, Tatsunori Hashimoto, Jason Eisner, and Richard Shin · 2023
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Aman Priyanshu, Supriti Vijay, Ayush Kumar, Rakshit Naidu, and Fatemehsadat Mireshghallah · 2023
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Locally differentially private document generation using zero shot prompting
Saiteja Utpala, Sara Hooker, and Pin-Yu Chen · 2023
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Decodingtrust: A comprehensive assessment of trustworthiness in GPT models
Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, and Bo Li · 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
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Privacy-preserving in-context learning for large language models
Tong Wu, Ashwinee Panda, Jiachen T. Wang, and Prateek Mittal · 2024
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