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Privacy concerns have attracted increasing attention in data-driven products due to the tendency of machine learning models to memorize sensitive training data.
RoBERTa: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Ctrl: A conditional transformer language model for controllable generation
Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong, and Richard Socher. 2019 · 1909
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2019 · 1910
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam D. Smith. 2006 · 2006
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No free lunch in data privacy
Daniel Kifer and Ashwin Machanavajjhala. 2011 · 2011
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Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate. 2013 · 2013
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Opinion 05/2014 on “Anonymisation Techniques”
Art. 29 WP. 2014 · 2014
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam D. Smith, and Abhradeep Thakurta. 2014 · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth. 2014 · 2014
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Differential privacy: An economic method for choosing epsilon
Justin Hsu, Marco Gaboardi, Andreas Haeberlen, Sanjeev Khanna, Arjun Narayan, Benjamin C Pierce, and Aaron Roth. 2014 · 2014
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Privbayes: private data release via bayesian networks
Jun Zhang, Graham Cormode, Cecilia M. Procopiuc, Divesh Srivastava, and Xiaokui Xiao. 2014 · 2014
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Deep learning with differential privacy
Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017 · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017 · 2017
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Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin. 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
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Differentially private generative adversarial network
Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, and Jiayu Zhou. 2018 · 2018
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Towards private synthetic text generation
Rishi Bommasani, Steven Wu, and Xanda Schofield. 2019 · 2019
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The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song. 2019 · 2019
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Leveraging hierarchical representations for preserving privacy and utility in text
Oluwaseyi Feyisetan, Tom Diethe, and Thomas Drake. 2019 · 2019
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PATE-GAN: generating synthetic data with differential privacy guarantees
James Jordon, Jinsung Yoon, and Mihaela van der Schaar. 2019 · 2019
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Improved precision and recall metric for assessing generative models
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Earlier work this paper cites.
Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
Cited alongside, same era.
Generative models for effective ML on private, decentralized datasets
Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, and Blaise Agüera y Arcas. 2020 · 2020
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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher 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 · 2020
Cited alongside, same era.
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
US Census Bureau. 2020 · 2020
Cited alongside, same era.
Privacy- and utility-preserving textual analysis via calibrated multivariate perturbations
Opacus: User-friendly differential privacy library in PyTorch
Ashkan Yousefpour, Igor Shilov, Alexandre Sablayrolles, Davide Testuggine, Karthik Prasad, Mani Malek, John Nguyen, Sayan Ghosh, Akash Bharadwaj, Jessica Zhao, Graham Cormode, and Ilya Mironov. 2021 · 2021
Later among the works it cites.
Do not let privacy overbill utility: Gradient embedding perturbation for private learning
Da Yu, Huishuai Zhang, Wei Chen, and Tie-Yan Liu. 2021a · 2021
Later among the works it cites.
Large scale private learning via low-rank reparametrization
Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, and Tie-Yan Liu. 2021b · 2021
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Differential privacy for text analytics via natural text sanitization
Xiang Yue, Minxin Du, Tianhao Wang, Yaliang Li, Huan Sun, and Sherman S. M. Chow. 2021 · 2021
Later among the works it cites.
Scalable and efficient training of large convolutional neural networks with differential privacy
Zhiqi Bu, Jialin Mao, and Shiyun Xu. 2022 · 2022
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Oluwaseyi Feyisetan, Borja Balle, Thomas Drake, and Tom Diethe. 2020 · 2020
Cited alongside, same era.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020 · 2020
Cited alongside, same era.
ER-AE: Differentially private text generation for authorship anonymization
Haohan Bo, Steven H. H. Ding, Benjamin C. M. Fung, and Farkhund Iqbal. 2021 · 2021
Cited alongside, same era.
Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot. 2021 · 2021
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 Brown, Dawn Song, Ulfar Erlingsson, et al. 2021 · 2021
Cited alongside, same era.
Numerical composition of differential privacy
Sivakanth Gopi, Yin Tat Lee, and Lukas Wutschitz. 2021a · 2021
Cited alongside, same era.
Numerical composition of differential privacy
Sivakanth Gopi, Yin Tat Lee, and Lukas Wutschitz. 2021b · 2021
Cited alongside, same era.
When differential privacy meets NLP: The devil is in the detail
Ivan Habernal. 2021 · 2021
Cited alongside, same era.
Closest in time.
Quantifying memorization across neural language models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr, and Chiyuan Zhang. 2022 · 2022
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Unlocking high-accuracy differentially private image classification through scale
Soham De, Leonard Berrada, Jamie Hayes, Samuel L Smith, and Borja Balle. 2022 · 2022
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Federated learning with formal differential privacy guarantees
Google. 2022 · 2022
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Bertopic: Neural topic modeling with a class-based tf-idf procedure
Maarten Grootendorst. 2022 · 2022
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dp-transformers: Training transformer models with differential privacy
Huseyin Inan, Andre Manoel, and Lukas Wutschitz. 2022 · 2022
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Deduplicating training data mitigates privacy risks in language models
Nikhil Kandpal, Eric Wallace, and Colin Raffel. 2022 · 2022
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Deduplicating training data makes language models better
Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, and Nicholas Carlini. 2022 · 2022
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When does differentially private learning not suffer in high dimensions?
Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin A Inan, Janardhan Kulkarni, Yin Tat Lee, and Abhradeep Guha Thakurta. 2022a · 2022
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Large language models can be strong differentially private learners
Xuechen Li, Florian Tramèr, Percy Liang, and Tatsunori Hashimoto. 2022b · 2022
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Differentially private language models for secure data sharing
Justus Mattern, Zhijing Jin, Benjamin Weggenmann, Bernhard Schoelkopf, and Mrinmaya Sachan. 2022 · 2022
Closest in time.
Sentence-level privacy for document embeddings
Casey Meehan, Khalil Mrini, and Kamalika Chaudhuri. 2022 · 2022
Closest in time.
Large scale transfer learning for differentially private image classification
Harsh Mehta, Abhradeep Thakurta, Alexey Kurakin, and Ashok Cutkosky. 2022 · 2022
Closest in time.
Privacy-preserving domain adaptation of semantic parsers
Fatemehsadat Mireshghallah, Richard Shin, Yu Su, Tatsunori Hashimoto, and Jason Eisner. 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
Closest in time.
Provably confidential language modelling
Xuandong Zhao, Lei Li, and Yu-Xiang Wang. 2022 · 2022
Closest in time.
Sanitizing sentence embeddings (and labels) for local differential privacy
Minxin Du, Xiang Yue, Sherman SM Chow, and Huan Sun. 2023 · 2023
Closest in time.