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Membership Inference attacks (MIAs) aim to predict whether a data sample was present in the training data of a machine learning model or not, and are widely used for assessing the privacy risks of language models.
Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, A. Terzis, and Florian Tramèr. 2021a · 1914
Earlier work this paper cites.
Label-only membership inference attacks
Christopher A. Choquette-Choo, Florian Tramer, Nicholas Carlini, and Nicolas Papernot. 2021 · 1974
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006 · 2006
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Ml privacy meter: Aiding regulatory compliance by quantifying the privacy risks of machine learning
Sasi Kumar Murakonda and R. Shokri. 2020 · 2007
Earlier work this paper cites.
Twitter sentiment classification using distant supervision
Alec Go, Richa Bhayani, and Lei Huang. 2009 · 2009
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Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D. Sarwate. 2013 · 2013
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta. 2014 · 2014
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2017 · 2017
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Membership inference attacks against machine learning models
R. Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2016 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin. 2018 · 2018
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Understanding membership inferences on well-generalized learning models
Yunhui Long, Vincent Bindschaedler, Lei Wang, Diyue Bu, Xiaofeng Wang, Haixu Tang, Carl A. Gunter, and Kai Chen. 2018 · 2018
Earlier work this paper cites.
Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha. 2018 · 2018
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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
Cited alongside, same era.
Memguard: Defending against black-box membership inference attacks via adversarial examples
Jinyuan Jia, Ahmed Salem, Michael Backes, Yang Zhang, and Neil Zhenqiang Gong. 2019 · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Cited alongside, same era.
White-box vs black-box: Bayes optimal strategies for membership inference
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, and Hervé Jégou. 2019 · 2019
Privacy regularization: Joint privacy-utility optimization in LanguageModels
Fatemehsadat Mireshghallah, Huseyin Inan, Marcello Hasegawa, Victor Rühle, Taylor Berg-Kirkpatrick, and Robert Sim. 2021 · 2021
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Counterfactual memorization in neural language models
Chiyuan Zhang, Daphne Ippolito, Katherine Lee, Matthew Jagielski, Florian Tramèr, and Nicholas Carlini. 2021 · 2021
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Relaxloss: Defending membership inference attacks without losing utility
Dingfan Chen, Ning Yu, and Mario Fritz. 2022 · 2022
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Deduplicating training data mitigates privacy risks in language models
Nikhil Kandpal, Eric Wallace, and Colin Raffel. 2022 · 2022
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Large language models can be strong differentially private learners
Xuechen Li, Florian Tramer, Percy Liang, and Tatsunori Hashimoto. 2022 · 2022
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Cited alongside, same era.
BERT-based lexical substitution
Wangchunshu Zhou, Tao Ge, Ke Xu, Furu Wei, and Ming Zhou. 2019 · 2019
Cited alongside, same era.
Membership inference attacks on sequence-to-sequence models: Is my data in your machine translation system?
Sorami Hisamoto, Matt Post, and Kevin Duh. 2020 · 2020
Cited alongside, same era.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020 · 2020
Cited alongside, same era.
Stolen memories: Leveraging model memorization for calibrated white-box membership inference
Klas Leino and Matt Fredrikson. 2020 · 2020
Cited alongside, same era.
Information leakage in embedding models
Congzheng Song and Ananth Raghunathan. 2020 · 2020
Cited alongside, same era.
Introducing a new privacy testing library in tensorflow
Shuang Song and David Marn. 2020 · 2020
Cited alongside, same era.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
Cited alongside, same era.
Florian Tramèr, Gautam Kamath, and Nicholas Carlini. 2022 · 2022
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On the importance of difficulty calibration in membership inference attacks
Lauren Watson, Chuan Guo, Graham Cormode, and Alexandre Sablayrolles. 2022 · 2022
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Enhanced membership inference attacks against machine learning models
Jiayuan Ye, Aadyaa Maddi, Sasi Kumar Murakonda, Vincent Bindschaedler, and Reza Shokri. 2022 · 2022
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Differentially private fine-tuning of language models
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, and Huishuai Zhang. 2022 · 2022
Later among the works it cites.
On the possibilities of ai-generated text detection
Souradip Chakraborty, Amrit Singh Bedi, Sicheng Zhu, Bang An, Dinesh Manocha, and Furong Huang. 2023 · 2023
Closest in time.
Paraphrasing evades detectors of ai-generated text, but retrieval is an effective defense
Kalpesh Krishna, Yixiao Song, Marzena Karpinska, John Wieting, and Mohit Iyyer. 2023 · 2023
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Smaller language models are better black-box machine-generated text detectors
Fatemehsadat Mireshghallah, Justus Mattern, Sicun Gao, Reza Shokri, and Taylor Berg-Kirkpatrick. 2023 · 2023
Closest in time.
Detectgpt: Zero-shot machine-generated text detection using probability curvature
Eric Mitchell, Yoonho Lee, Alexander Khazatsky, Christopher D. Manning, and Chelsea Finn. 2023 · 2023
Closest in time.
Membership Inference Attacks by Exploiting Loss Trajectory
Yiyong Liu, Zhengyu Zhao, Michael Backes, and Yang Zhang. 2022 · 2098
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