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In the text processing context, most ML models are built on word embeddings.
Segmentations-Leak: Membership Inference Attacks and Defenses in Semantic Image Segmentation
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Privacy in Deep Learning: A Survey
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GloVe: Global Vectors for Word Representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) . Association for Computational Linguistics, Doha, Qatar, 1532–1543
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word2vec parameter learning explained
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Neural Machine Translation of Rare Words with Subword Units. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Association for Computational Linguistics, Berlin, Germany, 1715–1725
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Enriching Word Vectors with Subword Information
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Membership Inference Attacks Against Machine Learning Models. In 2017 IEEE Symposium on Security and Privacy (SP) . 3–18
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Attention is All You Need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS’17) . Red Hook, NY, USA, 6000–6010
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. 2017 · 2017
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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
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Ahmed Salem, Yang Zhang, Mathias Humbert, Mario Fritz, and Michael Backes. 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
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When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning?
Gavin Brown, Mark Bun, Vitaly Feldman, Adam Smith, and Kunal Talwar. 2020 · 2020
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Extracting Training Data from Large Language Models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, Alina Oprea, and Colin Raffel. 2020 · 2020
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GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models. In Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security (Virtual Event, USA) (CCS ’20) . Association for Computing Machinery, New York, NY, USA, 343–362
Dingfan Chen, Ning Yu, Yang Zhang, and Mario Fritz. 2020 · 2020
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Label-Only Membership Inference Attacks
Christopher A. Choquette-Choo, Florian Tramer, Nicholas Carlini, and Nicolas Papernot. 2020 · 2020
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Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting. In 31st IEEE Computer Security Foundations Symposium, CSF 2018, Oxford, United Kingdom, July 9-12, 2018 . IEEE Computer Society, 268–282
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha. 2018a · 2018
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Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting. In 2018 IEEE 31st Computer Security Foundations Symposium (CSF) . 268–282
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha. 2018b · 2018
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Differential privacy has disparate impact on model accuracy. In NeurIPS 2019
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov. 2019 · 2019
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The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks. In USENIX Security 2019
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song. 2019 · 2019
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Gmail Smart Compose: Real-Time Assisted Writing (KDD ’19) . New York, NY, USA, 2287–2295
Mia Xu Chen, Benjamin N. Lee, Gagan Bansal, Yuan Cao, Shuyuan Zhang, Justin Lu, Jackie Tsay, Yinan Wang, Andrew M. Dai, Zhifeng Chen, Timothy Sohn, and Yonghui Wu. 2019 · 2019
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LOGAN: Membership Inference Attacks Against Generative Models
Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro. 2019 · 2019
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Demystifying the Membership Inference Attack. In 2019 12th CMI Conf. on Cybersecurity and Privacy (CMI) . 1–7
Paul Irolla and Grégory Châtel. 2019 · 2019
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Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning. In 2019 IEEE Symposium on Security and Privacy (SP) . 739–753
Milad Nasr, Reza Shokri, and Amir Houmansadr. 2019 · 2019
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Later among the works it cites.
Does Learning Require Memorization? A Short Tale about a Long Tail. In Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing (Chicago, IL, USA) (STOC 2020) . New York, NY, USA, 954–959
Vitaly Feldman. 2020 · 2020
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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
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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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ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning
Sasi Kumar Murakonda and Reza Shokri. 2020 · 2020
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Information Leakage in Embedding Models. In Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security (Virtual Event, USA) (CCS ’20) . Association for Computing Machinery, New York, NY, USA, 377–390
Congzheng Song and Ananth Raghunathan. 2020 · 2020
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Analyzing Information Leakage of Updates to Natural Language Models. In Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security . 363–375
Santiago Zanella-Béguelin, Lukas Wutschitz, Shruti Tople, Victor Rühle, Andrew Paverd, Olga Ohrimenko, Boris Köpf, and Marc Brockschmidt. 2020 · 2020
Later among the works it cites.
Node-Level Membership Inference Attacks Against Graph Neural Networks
Xinlei He, Rui Wen, Yixin Wu, Michael Backes, Yun Shen, and Yang Zhang. 2021 · 2021
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Membership Inference Attacks on Machine Learning: A Survey
Hongsheng Hu, Zoran Salcic, Gillian Dobbie, and Xuyun Zhang. 2021 · 2021
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Training Data Leakage Analysis in Language Models
Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, and Robert Sim. 2021 · 2021
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Membership Leakage in Label-Only Exposures
Zheng Li and Yang Zhang. 2021 · 2021
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Privacy Regularization: Joint Privacy-Utility Optimization in Language Models
Fatemehsadat Mireshghallah, Huseyin A. Inan, Marcello Hasegawa, Victor Rühle, Taylor Berg-Kirkpatrick, and Robert Sim. 2021 · 2021
Closest in time.
Membership Inference Attack on Graph Neural Networks
Iyiola E. Olatunji, Wolfgang Nejdl, and Megha Khosla. 2021 · 2021
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