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To protect the privacy of individuals whose data is being shared, it is of high importance to develop methods allowing researchers and companies to release textual data while providing formal privacy guarantees to its originators.
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 · 1901
Earlier work this paper cites.
Jinshuo Dong, Aaron Roth, and Weijie J Su. 2019 · 1905
Earlier work this paper cites.
Randomized response: A survey technique for eliminating evasive answer bias
Stanley L Warner. 1965 · 1965
Earlier work this paper cites.
Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
John Blitzer, Mark Dredze, and Fernando Pereira. 2007 · 2007
Earlier work this paper cites.
Robust de-anonymization of large sparse datasets
Arvind Narayanan and Vitaly Shmatikov. 2008 · 2008
Earlier work this paper cites.
Computational methods in authorship attribution
Moshe Koppel, Jonathan Schler, and Shlomo Argamon. 2009 · 2009
Earlier work this paper cites.
Boosting and differential privacy
Cynthia Dwork, Guy N Rothblum, and Salil Vadhan. 2010 · 2010
Earlier work this paper cites.
Local privacy and statistical minimax rates
J. C. Duchi, M. I. Jordan, and M. J. Wainwright. 2013 · 2013
Earlier work this paper cites.
Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate. 2013a · 2013
Earlier work this paper cites.
Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D. Sarwate. 2013b · 2013
Earlier work this paper cites.
Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta. 2014a · 2014
Earlier work this paper cites.
Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta. 2014b · 2014
Earlier work this paper cites.
The Algorithmic Foundations of Differential Privacy , volume 9
Cynthia Dwork and Aaron Roth. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath. 2015 · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016a · 2016
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016b · 2016
Earlier work this paper cites.
Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke. 2016 · 2016
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Concentrated differential privacy
Cynthia Dwork and Guy N Rothblum. 2016 · 2016
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The complexity of computing the optimal composition of differential privacy
Jack Murtagh and Salil Vadhan. 2016 · 2016
Earlier work this paper cites.
Blind de-anonymization attacks using social networks
Wei-Han Lee, Changchang Liu, Shouling Ji, Prateek Mittal, and Ruby B. Lee. 2017 · 2017
Earlier work this paper cites.
Rényi differential privacy
Ilya Mironov. 2017 · 2017
Earlier work this paper cites.
Convolutional neural networks for authorship attribution of short texts
Prasha Shrestha, Sebastian Sierra, Fabio González, Manuel Montes, Paolo Rosso, and Thamar Solorio. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Privacy-preserving neural representations of text
Maximin Coavoux, Shashi Narayan, and Shay B. Cohen. 2018 · 2018
Earlier work this paper cites.
Pate-gan: Generating synthetic data with differential privacy guarantees
James Jordon, Jinsung Yoon, and Mihaela Van Der Schaar. 2018 · 2018
Cited alongside, same era.
A4NT: Author attribute anonymity by adversarial training of neural machine translation
Rakshith Shetty, Bernt Schiele, and Mario Fritz. 2018 · 2018
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SynTF: Synthetic and differentially private term frequency vectors for privacy-preserving text mining
Benjamin Weggenmann and Florian Kerschbaum. 2018 · 2018
Cited alongside, same era.
Differentially private generative adversarial network
Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, and Jiayu Zhou. 2018 · 2018
Cited alongside, same era.
Heuristic authorship obfuscation
Janek Bevendorff, Martin Potthast, Matthias Hagen, and Benno Stein. 2019 · 2019
Cited alongside, same era.
Towards private synthetic text generation
Rishi Bommasani, Steven Wu, and Xanda Schofield. 2019 · 2019
Computing tight differential privacy guarantees using FFT
Antti Koskela, Joonas Jälkö, and Antti Honkela. 2020 · 2020
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Private post-gan boosting
Marcel Neunhoeffer, Steven Wu, and Cynthia Dwork. 2020 · 2020
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Information Leakage in Embedding Models , page 377–390. Association for Computing Machinery, New York, NY, USA
Congzheng Song and Ananth Raghunathan. 2020 · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
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Persistent anti-muslim bias in large language models
Abubakar Abid, Maheen Farooqi, and James Zou. 2021 · 2021
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Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Generalised differential privacy for text document processing
Natasha Fernandes, Mark Dras, and Annabelle McIver. 2019 · 2019
Cited alongside, same era.
Leveraging hierarchical representations for preserving privacy and utility in text
Oluwaseyi Feyisetan, Tom Diethe, and Thomas Drake. 2019a · 2019
Cited alongside, same era.
Leveraging hierarchical representations for preserving privacy and utility in text
Oluwaseyi Feyisetan, Tom Diethe, and Thomas Drake. 2019b · 2019
Cited alongside, same era.
Logan: Membership inference attacks against generative models
Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro. 2019 · 2019
Cited alongside, same era.
A girl has no name: Automated authorship obfuscation using mutant-x
Asad Mahmood, Faizan Ahmad, Zubair Shafiq, Padmini Srinivasan, and Fareed Zaffar. 2019 · 2019
Cited alongside, same era.
Privacy preserving text representation learning using bert
Walaa Alnasser, Ghazaleh Beigi, and Huan Liu. 2021 · 2021
Later among the works it cites.
Large-scale differentially private bert
Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, and Pasin Manurangsi. 2021 · 2021
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ER-AE: Differentially private text generation for authorship anonymization
Haohan Bo, Steven H. H. Ding, Benjamin C. M. Fung, and Farkhund Iqbal. 2021 · 2021
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Extracting training data from large language models
Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, and Colin Raffel. 2021 · 2021
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Numerical composition of differential privacy
Sivakanth Gopi, Yin Tat Lee, and Lukas Wutschitz. 2021 · 2021
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When differential privacy meets nlp: The devil is in the detail
Ivan Habernal. 2021 · 2021
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ADePT: Auto-encoder based differentially private text transformation
Satyapriya Krishna, Rahul Gupta, and Christophe Dupuy. 2021 · 2021
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Large language models can be strong differentially private learners
Xuechen Li, Florian Tramèr, Percy Liang, and Tatsunori Hashimoto. 2021 · 2021
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Towards understanding and mitigating social biases in language models
Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, and Ruslan Salakhutdinov. 2021 · 2021
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StylePTB: A compositional benchmark for fine-grained controllable text style transfer
Yiwei Lyu, Paul Pu Liang, Hai Pham, Eduard Hovy, Barnabás Póczos, Ruslan Salakhutdinov, and Louis-Philippe Morency. 2021 · 2021
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Mauve: Measuring the gap between neural text and human text using divergence frontiers
Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, and Zaid Harchaoui. 2021 · 2021
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Natural Language Understanding with Privacy-Preserving BERT , page 1488–1497. Association for Computing Machinery, New York, NY, USA
Chen Qu, Weize Kong, Liu Yang, Mingyang Zhang, Michael Bendersky, and Marc Najork. 2021 · 2021
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Few-shot text generation with natural language instructions
Timo Schick and Hinrich Schütze. 2021a · 2021
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Generating datasets with pretrained language models
Timo Schick and Hinrich Schütze. 2021b · 2021
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Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP
Timo Schick, Sahana Udupa, and Hinrich Schütze. 2021 · 2021
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What does it mean for a language model to preserve privacy?
Hannah Brown, Katherine Lee, Fatemehsadat Mireshghallah, Reza Shokri, and Florian Tramèr. 2022 · 2022
Closest in time.
How reparametrization trick broke differentially-private text representation learning
Ivan Habernal. 2022 · 2022
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The limits of word level differential privacy
Justus Mattern, Benjamin Weggenmann, and Florian Kerschbaum. 2022 · 2022
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DP-VAE: Human-readable text anonymization for online reviews with differentially private variational autoencoders
Benjamin Weggenmann, Valentin Rublack, Michael Andrejczuk, Justus Mattern, and Florian Kerschbaum. 2022 · 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 · 2022
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