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Ensuring the privacy of users whose data are used to train Natural Language Processing (NLP) models is necessary to build and maintain customer trust.
When do data mining results violate privacy?
Murat Kantarcioǧlu, Jiashun Jin, and Chris Clifton · 2004
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
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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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Embedding semantic similarity in tree kernels for domain adaptation of relation extraction
Barbara Plank and Alessandro Moschitti · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
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Domain adaptation of recurrent neural networks for natural language understanding
Aaron Jaech, Larry Heck, and Mari Ostendorf · 2016
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Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2017
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Learning differentially private recurrent language models, 2017
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 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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Author obfuscation using generalised differential privacy
Natasha Fernandes, Mark Dras, and Annabelle McIver · 2018
Cited alongside, same era.
Practical differentially private top-k selection with pay-what-you-get composition
David Durfee and Ryan M Rogers · 2019
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Leveraging hierarchical representations for preserving privacy and utility in text
Oluwaseyi Feyisetan, Tom Diethe, and Thomas Drake · 2019
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dpugc: Learn differentially private representation for user generated contents
XS Vu, SN Tran, and L Jiang · 2019
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Clinical text classification with rule-based features and knowledge-guided convolutional neural networks
Liang Yao, Chengsheng Mao, and Yuan Luo · 2019
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Preserving privacy in analyses of textual data, 2020
Thomas Diethe · 2020
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Privacy- and utility-preserving textual analysis via calibrated multivariate perturbations
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Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
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Er-ae: Differentially-private text generation for authorship anonymization
Haohan Bo, Steven HH Ding, Benjamin Fung, and Farkhund Iqbal · 2019
Cited alongside, same era.
Oluwaseyi Feyisetan, Borja Balle, Thomas Drake, and Tom Diethe · 2020
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Bag of tricks for efficient text classification
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov · 2068
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