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Natural Language Understanding (NLU) models can be trained on sensitive information such as phone numbers, zip-codes etc.
Membership model inversion attacks for deep networks
Samyadeep Basu, Rauf Izmailov, and Chris Mesterharm. 2019 · 1910
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Error detecting and error correcting codes
Richard W Hamming. 1950 · 1950
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Expanding the scope of the atis task: The atis-3 corpus
Deborah A. Dahl, Madeleine Bates, Michael Brown, William Fisher, Kate Hunicke-Smith, David Pallett, Christine Pao, Alexander Rudnicky, and Elizabeth Shriber. 1994 · 1994
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Natural language question answering: the view from here
Lynette Hirschman and Robert Gaizauskas. 2001 · 2001
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Natural language understanding using statistical machine translation
Klaus Macherey, Franz Josef Och, and Hermann Ney. 2001 · 2001
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Information leakage in embedding models
Congzheng Song and Ananth Raghunathan. 2020 · 2004
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Fast data anonymization with low information loss
Gabriel Ghinita, Panagiotis Karras, Panos Kalnis, and Nikos Mamoulis. 2007 · 2007
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A survey automatic text summarization
Oguzhan Tas and Farzad Kiyani. 2007 · 2007
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Extracting training data from large language models
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Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing
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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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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole. 2016 · 2016
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End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. 2016 · 2016
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The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Jernej Kos, Úlfar Erlingsson, and Dawn Song. 2018 · 2018
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Alice Coucke, Alaa Saade, Adrien Ball, Théodore Bluche, Alexandre Caulier, David Leroy, Clément Doumouro, Thibault Gisselbrecht, Francesco Caltagirone, Thibaut Lavril, et al. 2018 · 2018
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Advances in pre-training distributed word representations
Tomas Mikolov, Edouard Grave, Piotr Bojanowski, Christian Puhrsch, and Armand Joulin. 2018 · 2018
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Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning
Milad Nasr, Reza Shokri, and Amir Houmansadr. 2019 · 2019
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Auditing data provenance in text-generation models
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Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al. 2017 · 2017
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Logan: evaluating privacy leakage of generative models using generative adversarial networks
Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro. 2017 · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017 · 2017
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Congzheng Song and Vitaly Shmatikov. 2019 · 2019
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Demystifying membership inference attacks in machine learning as a service
Stacey Truex, Ling Liu, Mehmet Emre Gursoy, Lei Yu, and Wenqi Wei. 2019 · 2019
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Benchmarking natural language understanding services for building conversational agents
Pawel Swietojanski Xingkun Liu, Arash Eshghi and Verena Rieser. 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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