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Differentially private stochastic gradient descent (DP-SGD) adds noise to gradients in back-propagation, safeguarding training data from privacy leakage, particularly membership inference.
Deep Learning with Gaussian Differential Privacy
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Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
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Practical Secure Aggregation for Privacy-Preserving Machine Learning. In CCS . 1175–1191
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Membership Inference Attacks Against Machine Learning Models. In S&P . 3–18
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Attention is All You Need. In NeurIPS . 5998–6008
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Borja Balle and Yu-Xiang Wang. 2018 · 2018
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MVG Mechanism: Differential Privacy under Matrix-Valued Query. In CCS . 230–246
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Adversarial Removal of Demographic Attributes from Text Data. In EMNLP . 11–21
Yanai Elazar and Yoav Goldberg. 2018 · 2018
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Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations. In CCS . 619–633
Karan Ganju, Qi Wang, Wei Yang, Carl A. Gunter, and Nikita Borisov. 2018 · 2018
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Learning Differentially Private Recurrent Language Models. In ICLR (Poster) . 14 pages
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. 2018 · 2018
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Improving language understanding by generative pre-training
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Numerical Composition of Differential Privacy. In NeurIPS . 11631–11642
Sivakanth Gopi, Yin Tat Lee, and Lukas Wutschitz. 2021 · 2021
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Differentially Private Binary- and Matrix-Valued Data Query: An XOR Mechanism
Tianxi Ji, Pan Li, Emre Yilmaz, Erman Ayday, Yanfang Ye, and Jinyuan Sun. 2021 · 2021
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The Lipschitz Constant of Self-Attention. In ICML , Vol. 139. 5562–5571
Hyunjik Kim, George Papamakarios, and Andriy Mnih. 2021 · 2021
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Does BERT Pretrained on Clinical Notes Reveal Sensitive Data?. In NAACL-HLT . 946–959
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A4NT: Author Attribute Anonymity by Adversarial Training of Neural Machine Translation. In USENIX Security . 1633–1650
Rakshith Shetty, Bernt Schiele, and Mario Fritz. 2018 · 2018
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SynTF: Synthetic and Differentially Private Term Frequency Vectors for Privacy-Preserving Text Mining. In SIGIR . 305–314
Benjamin Weggenmann and Florian Kerschbaum. 2018 · 2018
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Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting. In CSF . 268–282
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha. 2018 · 2018
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The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks. In USENIX Security . 267–284
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. In NAACL-HLT . 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity. In SODA . 2468–2479
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Abhradeep Thakurta. 2019 · 2019
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Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text. In ICDM . 210–219
Oluwaseyi Feyisetan, Tom Diethe, and Thomas Drake. 2019 · 2019
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Natural Language Understanding with Privacy-Preserving BERT. In CIKM . 1488–1497
Chen Qu, Weize Kong, Liu Yang, Mingyang Zhang, Michael Bendersky, and Marc Najork. 2021 · 2021
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Systematic Evaluation of Privacy Risks of Machine Learning Models. In USENIX Security . 2615–2632
Liwei Song and Prateek Mittal. 2021 · 2021
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Improved Matrix Gaussian Mechanism for Differential Privacy
Jungang Yang, Liyao Xiang, Weiting Li, Wei Liu, and Xinbing Wang. 2021 · 2021
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Opacus: User-Friendly Differential Privacy Library in PyTorch
Ashkan Yousefpour, Igor Shilov, Alexandre Sablayrolles, Davide Testuggine, Karthik Prasad, Mani Malek, John Nguyen, Sayan Ghosh, Akash Bharadwaj, Jessica Zhao, Graham Cormode, and Ilya Mironov. 2021 · 2021
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Large Scale Private Learning via Low-rank Reparametrization. In ICML . 12208–12218
Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, and Tie-Yan Liu. 2021 · 2021
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Differential Privacy for Text Analytics via Natural Text Sanitization. In Findings of ACL/IJCNLP . 3853–3866
Xiang Yue, Minxin Du, Tianhao Wang, Yaliang Li, Huan Sun, and Sherman S. M. Chow. 2021 · 2021
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Normalization of Language Embeddings for Cross-Lingual Alignment. In ICLR (Poster) . 32 pages
Prince Osei Aboagye, Yan Zheng, Chin-Chia Michael Yeh, Junpeng Wang, Wei Zhang, Liang Wang, Hao Yang, and Jeff M. Phillips. 2022 · 2022
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The 2020 Census Disclosure Avoidance System TopDown Algorithm
John Abowd, Robert Ashmead, Ryan Cumings-Menon, Simson Garfinkel, Micah Heineck, Christine Heiss, Robert Johns, Daniel Kifer, Philip Leclerc, Ashwin Machanavajjhala, Brett Moran, William Sexton, Matthew Spence, and Pavel Zhuravlev. 2022 · 2022
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Large-Scale Differentially Private BERT. In Findings of EMNLP . 6481–6491
Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, and Pasin Manurangsi. 2022 · 2022
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LoRA: Low-Rank Adaptation of Large Language Models. In ICLR (Poster) . 13 pages
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
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A Survey on Deep Learning for Named Entity Recognition
Jing Li, Aixin Sun, Jianglei Han, and Chenliang Li. 2022a · 2022
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Sentence-level Privacy for Document Embeddings. In ACL . 3367–3380
Casey Meehan, Khalil Mrini, and Kamalika Chaudhuri. 2022 · 2022
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Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?. In EMNLP . 11048–11064
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
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Composition of Differential Privacy & Privacy Amplification by Subsampling
Thomas Steinke. 2022 · 2022
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Differentially Private Fine-tuning of Language Models. In ICLR (Poster) . 19 pages
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
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Locally Differentially Private Sparse Vector Aggregation. In S&P . 422–439
Mingxun Zhou, Tianhao Wang, T.-H. Hubert Chan, Giulia Fanti, and Elaine Shi. 2022 · 2022
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Sanitizing Sentence Embeddings (and Labels) for Local Differential Privacy. In WWW . 2349–2359
Minxin Du, Xiang Yue, Sherman S. M. Chow, and Huan Sun. 2023 · 2023
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BERT base model (uncased)
Hugging Face. 2023 · 2023
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SoK: Cryptographic Neural-Network Computation. In S&P . 497–514
Lucien K. L. Ng and Sherman S. M Chow. 2023 · 2023
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Does Label Differential Privacy Prevent Label Inference Attacks?. In AISTAST . 4336–4347
Ruihan Wu, Jin Peng Zhou, Kilian Q. Weinberger, and Chuan Guo. 2023 · 2023
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Matrix Gaussian Mechanisms for Differentially-Private Learning
Jungang Yang, Liyao Xiang, Jiahao Yu, Xinbing Wang, Bin Guo, Zhetao Li, and Baochun Li. 2023 · 2023
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