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Deep learning-based language models have achieved state-of-the-art results in a number of applications including sentiment analysis, topic labelling, intent classification and others.
Sex, Syntax, and Semantics
Lera Boroditsky and Lauren A. Schmidt. 2000 · 2000
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Robust de-anonymization of large sparse datasets
Arvind Narayanan and Vitaly Shmatikov. 2008 · 2008
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Anonymizing transaction databases for publication
Yabo Xu, Ke Wang, Ada Wai Chee Fu, and Philip S. Yu. 2008 · 2008
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Joint Link-Attribute User Identity Resolution in Online Social Networks Categories and Subject Descriptors
Sergey Bartunov, Anton Korshunov, Seung-taek Park, Wonho Ryu, and Hyungdong Lee. 2012 · 2012
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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 B. Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, and Colin Raffel. 2020 · 2012
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Beware of what you share: Inferring home location in social networks
Tatiana Pontes, Gabriel Magno, Marisa Vasconcelos, Aditi Gupta, Jussara Almeida, Ponnurangam Kumaraguru, and Virgilio Almeida. 2012 · 2012
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On the identity anonymization of high-dimensional rating data
Xiaoxun Sun, Hua Wang, and Yanchun Zhang. 2012 · 2012
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth. 2013 · 2013
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Exploiting innocuous activity for correlating users across sites
Oana Goga, Howard Lei, Sree Hari Krishnan Parthasarathi, Gerald Friedland, Robin Sommer, and Renata Teixeira. 2013 · 2013
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart. 2015 · 2015
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User Review Sites as a Resource for Large-Scale Sociolinguistic Studies
Dirk Hovy, Anders Johannsen, and Anders Søgaard. 2015 · 2015
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Conservative or liberal? Personalized differential privacy
Z. Jorgensen, T. Yu, and G. Cormode. 2015 · 2015
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov. 2015 · 2015
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Social Media Research: A Guide to Ethics
Leanne Townsend and Claire Wallace. 2016 · 2016
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Heterogeneous differential privacy
Mohammad Alaggan, Sébastien Gambs, and Anne-Marie Kermarrec. 2017 · 2017
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. 2017 · 2017
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Machine learning models that remember too much
Congzheng Song, Thomas Ristenpart, and Vitaly Shmatikov. 2017 · 2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Generalised Differential Privacy for Text Document Processing
Natasha Fernandes, Mark Dras, and Annabelle McIver. 2019 · 2019
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Preserving differential privacy in adversarial learning with provable robustness
Nhat Hai Phan, My T. Thai, Ruoming Jin, Han Hu, and Dejing Dou. 2019 · 2019
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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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Privacy-preserving neural representations of text
Maximin Coavoux, Shashi Narayan, and Shay B Cohen. 2018a · 2018
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Privacy-preserving Neural Representations of Text
Maximin Coavoux, Shashi Narayan, and Shay B. Cohen. 2018b · 2018
Cited alongside, same era.
Embedding text in hyperbolic spaces
Bhuwan Dhingra, Christopher J. Shallue, Mohammad Norouzi, Andrew M. Dai, and George E. Dahl. 2018 · 2018
Cited alongside, same era.
Towards robust and privacy-preserving text representations
Yitong Li, Timothy Baldwin, and Trevor Cohn. 2018 · 2018
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Towards Demystifying Membership Inference Attacks
Stacey Truex, Ling Liu, Mehmet Emre Gursoy, Lei Yu, and Wenqi Wei. 2018 · 2018
Cited alongside, same era.
Privacy preserving text representation learning
Ghazaleh Beigi, Kai Shu, Ruocheng Guo, Suhang Wang, and Huan Liu. 2019 · 2019
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Privacy- And utility-preserving textual analysis via calibrated multivariate perturbations
Oluwaseyi Feyisetan, Borja Balle, Thomas Drake, and Tom Diethe. 2020 · 2020
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Not All Attributes are Created Equal: dX -Private Mechanisms for Linear Queries
Parameswaran Kamalaruban, Victor Perrier, Hassan Jameel Asghar, and Mohamed Ali Kaafar. 2020 · 2020
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Stolen memories: Leveraging model memorization for calibrated white-box membership inference
Klas Leino and Matt Fredrikson. 2020 · 2020
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Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness
Lingjuan Lyu, Xuanli He, and Yitong Li. 2020 · 2020
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Information Leakage in Embedding Models
Congzheng Song and Ananth Raghunathan. 2020 · 2020
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Compositional demographic word embeddings
Charles Welch, Jonathan K. Kummerfeld, Verónica Pérez-Rosas, and Rada Mihalcea. 2020 · 2020
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The secret revealer: Generative model-inversion attacks against deep neural networks
Yuheng Zhang, Ruoxi Jia, Hengzhi Pei, Wenxiao Wang, Bo Li, and Dawn Song. 2020 · 2020
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