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Privacy preservation remains a key challenge in data mining and Natural Language Understanding (NLU).
Calibrating Noise to Sensitivity in Private Data Analysis. In Proceedings of the 3rd Theory of Cryptography Conference . 265–284
C. Dwork, F. McSherry, Kobbi Nissim, and A. D. Smith. 2006 · 2006
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What Can We Learn Privately?
S. Kasiviswanathan, H. Lee, K. Nissim, Sofya Raskhodnikova, and A. D. Smith. 2008 · 2008
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t-Plausibility: Generalizing Words to Desensitize Text
B. Anandan, C. Clifton, Wenxin Jiang, M. Murugesan, Pedro Pastrana-Camacho, and L. Si. 2012 · 2012
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Broadening the Scope of Differential Privacy Using Metrics. In Proceedings of the 13th International Symposium on Privacy Enhancing Technologies . 82–102
K. Chatzikokolakis, M. Andrés, N. E. Bordenabe, and C. Palamidessi. 2013 · 2013
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Distributed Representations of Words and Phrases and their Compositionality. In Advances in Neural Information Processing Systems . 3111–3119
Tomas Mikolov, Ilya Sutskever, Kai Chen, G. S. Corrado, and J. Dean. 2013 · 2013
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Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing . 1631–1642
R. Socher, Alex Perelygin, J. Wu, Jason Chuang, Christopher D. Manning, A. Ng, and Christopher Potts. 2013 · 2013
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov. 2015 · 2015
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Character-level Convolutional Networks for Text Classification. In Advances in Neural Information Processing Systems . 649–657
X. Zhang, J. Zhao, and Y. LeCun. 2015 · 2015
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Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books
Y. Zhu, Ryan Kiros, R. Zemel, R. Salakhutdinov, R. Urtasun, A. Torralba, and S. Fidler. 2015 · 2015
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Deep Learning with Differential Privacy
M. Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and L. Zhang. 2016 · 2016
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Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Y. Wu, Mike Schuster, Z. Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, M. Krikun, Yuan Cao, Q. Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, M. Johnson, X. Liu, L. Kaiser, S. Gouws, Y. Kato, Taku Kudo, H. Kazawa, K. Stevens, G. Kurian, Nishant Patil, W. Wang, C. Young, J. Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, G. S. Corrado, Macduff Hughes, and J. Dean. 2016 · 2016
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Learning from User Interactions in Personal Search via Attribute Parameterization. In Proceedings of the 10th ACM International Conference on Web Search and Data Mining . 791–799
Michael Bendersky, X. Wang, Donald Metzler, and Marc Najork. 2017 · 2017
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Plausible Deniability for Privacy-Preserving Data Synthesis
Vincent Bindschaedler, R. Shokri, and Carl A. Gunter. 2017 · 2017
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Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics
H. McMahan, Eider Moore, D. Ramage, S. Hampson, and Blaise Agüera y Arcas. 2017 · 2017
Cited alongside, same era.
Locally Differentially Private Protocols for Frequency Estimation. In Proceedings of the 26th USENIX Conference on Security Symposium . 729–745
Tianhao Wang, J. Blocki, N. Li, and S. Jha. 2017 · 2017
Cited alongside, same era.
Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics
Xi Wu, Fengan Li, A. Kumar, K. Chaudhuri, S. Jha, and J. Naughton. 2017 · 2017
Cited alongside, same era.
Ngram2vec: Learning Improved Word Representations from Ngram Co-occurrence Statistics. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing . 244–253
Zhe Zhao, T. Liu, Shen Li, Bofang Li, and X. Du. 2017 · 2017
Cited alongside, same era.
Protection Against Reconstruction and Its Applications in Private Federated Learning
Certified Robustness to Adversarial Examples with Differential Privacy
Mathias Lécuyer, Vaggelis Atlidakis, Roxana Geambasu, D. Hsu, and Suman Jana. 2019 · 2019
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Multi-view Embedding-based Synonyms for Email Search. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval . 575–584
Cheng Li, Mingyang Zhang, Michael Bendersky, H. Deng, Donald Metzler, and Marc Najork. 2019 · 2019
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RoBERTa: A Robustly Optimized BERT Pretraining Approach
Y. Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov. 2019 · 2019
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BERT with History Answer Embedding for Conversational Question Answering. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval . 1133–1136
Chen Qu, Liu Yang, Minghui Qiu, W. Croft, Yongfeng Zhang, and Mohit Iyyer. 2019 · 2019
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Abhishek Bhowmick, John C. Duchi, J. Freudiger, G. Kapoor, and Ryan Rogers. 2018 · 2018
Cited alongside, same era.
Privacy-preserving Neural Representations of Text. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . 1–10
Maximin Coavoux, Shashi Narayan, and Shay B. Cohen. 2018 · 2018
Cited alongside, same era.
Towards Robust and Privacy-preserving Text Representations. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) . 25–30
Yitong Li, Timothy Baldwin, and Trevor Cohn. 2018 · 2018
Cited alongside, same era.
Learning Differentially Private Recurrent Language Models. In Proceedings of the 6th International Conference on Learning Representations
H. McMahan, D. Ramage, Kunal Talwar, and L. Zhang. 2018 · 2018
Cited alongside, same era.
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2018 · 2018
Cited alongside, same era.
SynTF: Synthetic and Differentially Private Term Frequency Vectors for Privacy-Preserving Text Mining. In Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval . 305–314
Benjamin Weggenmann and Florian Kerschbaum. 2018 · 2018
Cited alongside, same era.
From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management . 497–506
Hamed Zamani, M. Dehghani, W. Croft, E. Learned-Miller, and J. Kamps. 2018 · 2018
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
XLNet: Generalized Autoregressive Pretraining for Language Understanding. In Advances in Neural Information Processing Systems . 5753–5763
Z. Yang, Zihang Dai, Y. Yang, J. Carbonell, R. Salakhutdinov, and Quoc V. Le. 2019 · 2019
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Generic Intent Representation in Web Search. In SIGIR
Hongfei Zhang, Xia Song, Chenyan Xiong, C. Rosset, P. Bennett, Nick Craswell, and Saurabh Tiwary. 2019 · 2019
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Language Models are Few-Shot Learners
T. Brown, B. Mann, Nick Ryder, Melanie Subbiah, J. Kaplan, P. Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, G. Krüger, Tom Henighan, R. Child, Aditya Ramesh, D. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, E. Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, J. Clark, Christopher Berner, Sam McCandlish, A. Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
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Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations. In Proceedings of the 13th International Conference on Web Search and Data Mining . 178–186
Oluwaseyi Feyisetan, Borja Balle, Thomas Drake, and Tom Diethe. 2020 · 2020
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Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness. In Findings of the Association for Computational Linguistics: EMNLP 2020 . 2355–2365
L. Lyu, Xuanli He, and Yitong Li. 2020a · 2020
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Threats to Federated Learning: A Survey
Lingjuan Lyu, Han Yu, and Q. Yang. 2020c · 2020
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Towards Fair and Privacy-Preserving Federated Deep Models
Lingjuan Lyu, Jiangshan Yu, Karthik Nandakumar, Yitong Li, Xingjun Ma, Jiong Jin, H. Yu, and K. S. Ng. 2020d · 2020
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Privacy Risks of General-Purpose Language Models
Xudong Pan, Mi Zhang, Shouling Ji, and Min Yang. 2020 · 2020
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Information Leakage in Embedding Models
Congzheng Song and Ananth Raghunathan. 2020 · 2020
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