Fetching the paper…
Reading the bibliography…
With the increasing applications of language models, it has become crucial to protect these models from leaking private information.
Differentially private learning with adaptive clipping
Om Thakkar, Galen Andrew, and H Brendan McMahan. 2019 · 1905
Earlier work this paper cites.
DP-LSSGD: A stochastic optimization method to lift the utility in privacy-preserving ERM
Bao Wang, Quanquan Gu, March Boedihardjo, Farzin Barekat, and Stanley J. Osher. 2019 · 1906
Earlier work this paper cites.
Tempered sigmoid activations for deep learning with differential privacy
Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, and Úlfar Erlingsson. 2020 · 2007
Earlier work this paper cites.
Training production language models without memorizing user data
Swaroop Ramaswamy, Om Thakkar, Rajiv Mathews, Galen Andrew, H Brendan McMahan, and Françoise Beaufays. 2020 · 2009
Earlier work this paper cites.
Differentially private deep learning with direct feedback alignment
Jaewoo Lee and Daniel Kifer. 2020 · 2010
Earlier work this paper cites.
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. 2020 · 2012
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al. 2014 · 2014
Earlier work this paper cites.
Differential privacy: Now it’s getting personal
Hamid Ebadi, David Sands, and Gerardo Schneider. 2015 · 2015
Earlier work this paper cites.
Conservative or liberal? personalized differential privacy
Zach Jorgensen, Ting Yu, and Graham Cormode. 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. 2016 · 2016
Earlier work this paper cites.
Differential privacy in control and network systems
Jorge Cortés, Geir E Dullerud, Shuo Han, Jerome Le Ny, Sayan Mitra, and George J Pappas. 2016 · 2016
Earlier work this paper cites.
Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Cited alongside, same era.
One-sided differential privacy
Stelios Doudalis, Ios Kotsogiannis, Samuel Haney, Ashwin Machanavajjhala, and Sharad Mehrotra. 2017 · 2017
Cited alongside, same era.
spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing
Matthew Honnibal and Ines Montani. 2017 · 2017
Cited alongside, same era.
Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2017 · 2017
Cited alongside, same era.
Rényi differential privacy
Ilya Mironov. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song. 2019 · 2019
Later among the works it cites.
Generalised differential privacy for text document processing
Natasha Fernandes, Mark Dras, and Annabelle McIver. 2019 · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Later among the works it cites.
Privacy guarantees for de-identifying text transformations
David Ifeoluwa Adelani, Ali Davody, Thomas Kleinbauer, and Dietrich Klakow. 2020 · 2020
Later among the works it cites.
Differentially private language models benefit from public pre-training
Gavin Kerrigan, Dylan Slack, and Jens Tuyls. 2020 · 2020
Later among the works it cites.
A differentially private text perturbation method using regularized mahalanobis metric
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
The us census bureau adopts differential privacy
John M Abowd. 2018 · 2018
Cited alongside, same era.
Privacy amplification by subsampling: Tight analyses via couplings and divergences
Borja Balle, Gilles Barthe, and Marco Gaboardi. 2018 · 2018
Cited alongside, same era.
Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. 2018 · 2018
Cited alongside, same era.
Scalable private learning with pate
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson. 2018 · 2018
Cited alongside, same era.
Zero-shot dialog generation with cross-domain latent actions
Tiancheng Zhao and Maxine Eskenazi. 2018 · 2018
Cited alongside, same era.
Zekun Xu, Abhinav Aggarwal, Oluwaseyi Feyisetan, and Nathanael Teissier. 2020 · 2020
Later among the works it cites.
Rajitha Hathurusinghe, Isar Nejadgholi, and Miodrag Bolic. 2021 · 2021
Closest in time.
Differentially-private text generation via text preprocessing to reduce utility loss
Taisho Sasada, Masataka Kawai, Yuzo Taenaka, Doudou Fall, and Youki Kadobayashi. 2021 · 2021
Closest in time.
Dplis: Boosting utility of differentially private deep learning via randomized smoothing
Wenxiao Wang, Tianhao Wang, Lun Wang, Nanqing Luo, Pan Zhou, Dawn Song, and Ruoxi Jia. 2021 · 2021
Closest in time.
Differential privacy for text analytics via natural text sanitization
Xiang Yue, Minxin Du, Tianhao Wang, Yaliang Li, Huan Sun, and Sherman SM Chow. 2021 · 2021
Closest in time.
Just fine-tune twice: Selective differential privacy for large language models
Weiyan Shi, Si Chen, Chiyuan Zhang, Ruoxi Jia, and Zhou Yu. 2022 · 2022
Closest in time.