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As more and more pre-trained language models adopt on-cloud deployment, the privacy issues grow quickly, mainly for the exposure of plain-text user data (e.g., search history, medical record, bank account).
Simple applications of BERT for ad hoc document retrieval
Wei Yang, Haotian Zhang, and Jimmy Lin. 2019c · 1903
Earlier work this paper cites.
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
Earlier work this paper cites.
idlg: Improved deep leakage from gradients
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen. 2020 · 2001
Earlier work this paper cites.
Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition
Erik F. Tjong Kim Sang and Fien De Meulder. 2003 · 2003
Earlier work this paper cites.
The PASCAL recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005 · 2005
Earlier work this paper cites.
Automatically constructing a corpus of sentential paraphrases
William B. Dolan and Chris Brockett. 2005 · 2005
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam D. Smith. 2006 · 2006
Earlier work this paper cites.
The second pascal recognising textual entailment challenge
R Bar Haim, Ido Dagan, Bill Dolan, Lisa Ferro, Danilo Giampiccolo, Bernardo Magnini, and Idan Szpektor. 2006 · 2006
Earlier work this paper cites.
The third PASCAL recognizing textual entailment challenge
Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, and Bill Dolan. 2007 · 2007
Earlier work this paper cites.
Privacy protection in personalized search
Xuehua Shen, Bin Tan, and ChengXiang Zhai. 2007 · 2007
Earlier work this paper cites.
The fifth PASCAL recognizing textual entailment challenge
Luisa Bentivogli, Bernardo Magnini, Ido Dagan, Hoa Trang Dang, and Danilo Giampiccolo. 2009 · 2009
Earlier work this paper cites.
Fully homomorphic encryption using ideal lattices
Craig Gentry. 2009 · 2009
Earlier work this paper cites.
The pii problem: Privacy and a new concept of personally identifiable information
Paul M Schwartz and Daniel J Solove. 2011 · 2011
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts. 2013 · 2013
Earlier work this paper cites.
Secure secondary use of clinical data with cloud-based nlp services
J Christoph, L Griebel, I Leb, I Engel, F Köpcke, D Toddenroth, H-U Prokosch, J Laufer, K Marquardt, and M Sedlmayr. 2015 · 2015
Cited alongside, same era.
Efficient exact gradient update for training deep networks with very large sparse targets
Pascal Vincent, Alexandre de Brébisson, and Xavier Bouthillier. 2015 · 2015
Cited alongside, same era.
Deep learning with differential privacy
Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
Cited alongside, same era.
Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016 · 2016
Cited alongside, same era.
Bridging nonlinearities and stochastic regularizers with gaussian error linear units
Dan Hendrycks and Kevin Gimpel. 2016 · 2016
Cited alongside, same era.
BERT post-training for review reading comprehension and aspect-based sentiment analysis
Hu Xu, Bing Liu, Lei Shu, and Philip S. Yu. 2019a · 2019
Later among the works it cites.
Understanding and improving layer normalization
Jingjing Xu, Xu Sun, Zhiyuan Zhang, Guangxiang Zhao, and Junyang Lin. 2019b · 2019
Later among the works it cites.
End-to-end open-domain question answering with bertserini
Wei Yang, Yuqing Xie, Aileen Lin, Xingyu Li, Luchen Tan, Kun Xiong, Ming Li, and Jimmy Lin. 2019b · 2019
Later among the works it cites.
MP2ML: A mixed-protocol machine learning framework for private inference
Fabian Boemer, Rosario Cammarota, Daniel Demmler, Thomas Schneider, and Hossein Yalame. 2020 · 2020
Later among the works it cites.
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
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Squad: 100, 000+ questions for machine comprehension of text
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Cited alongside, same era.
SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation
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Cited alongside, same era.
Homomorphic encryption for arithmetic of approximate numbers
Jung Hee Cheon, Andrey Kim, Miran Kim, and Yong Soo Song. 2017 · 2017
Cited alongside, same era.
A broad-coverage challenge corpus for sentence understanding through inference
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Cited alongside, same era.
ngraph-he2: A high-throughput framework for neural network inference on encrypted data
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Cited alongside, same era.
ngraph-he: a graph compiler for deep learning on homomorphically encrypted data
Fabian Boemer, Yixing Lao, Rosario Cammarota, and Casimir Wierzynski. 2019b · 2019
Cited alongside, same era.
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
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TextHide: Tackling data privacy in language understanding tasks
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Later among the works it cites.
Differentially private language models benefit from public pre-training
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Differentially private representation for NLP: formal guarantee and an empirical study on privacy and fairness
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Later among the works it cites.
Microsoft SEAL (release 3.6)
SEAL. 2020 · 2020
Later among the works it cites.
Deep leakage from gradients
Ligeng Zhu and Song Han. 2020 · 2020
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Natural language understanding with privacy-preserving bert
Chen Qu, Weize Kong, Liu Yang, Mingyang Zhang, Michael Bendersky, and Marc Najork. 2021 · 2021
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