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Large Language Models (LLMs) pose significant privacy risks, potentially leaking training data due to implicit memorization.
The enron corpus: A new dataset for email classification research
Bryan Klimt and Yiming Yang. 2004 · 2004
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Differential privacy
Cynthia Dwork. 2006 · 2006
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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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Microsoft Presidio: Context aware, pluggable and customizable pii anonymization service for text and images
Omri Mendels, Coby Peled, Nava Vaisman Levy, Sharon Hart, Tomer Rosenthal, Limor Lahiani, et al. 2018 · 2018
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The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song. 2019 · 2019
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Echr: Legal corpus for argument mining
Prakash Poudyal, Jaromír Šavelka, Aagje Ieven, Marie Francine Moens, Teresa Goncalves, and Paulo Quaresma. 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, et al. 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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Quantifying privacy risks of masked language models using membership inference attacks
Fatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal, Taylor Berg-Kirkpatrick, and Reza Shokri. 2022a · 2022
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An empirical analysis of memorization in fine-tuned autoregressive language models
Fatemehsadat Mireshghallah, Archit Uniyal, Tianhao Wang, David K Evans, and Taylor Berg-Kirkpatrick. 2022b · 2022
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dp-transformers: Training transformer models with differential privacy
Lukas Wutschitz, Huseyin A. Inan, and Andre Manoel. 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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Quantifying memorization across neural language models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, and Chiyuan Zhang. 2023 · 2023
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Ideas are dimes a dozen: Large language models for idea generation in innovation
Karan Girotra, Lennart Meincke, Christian Terwiesch, and Karl T Ulrich. 2023 · 2023
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Analyzing leakage of personally identifiable information in language models
Nils Lukas, Ahmed Salem, Robert Sim, Shruti Tople, Lukas Wutschitz, and Santiago Zanella-Beguelin. 2023 · 2023
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Membership inference attacks against language models via neighbourhood comparison
Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, Bernhard Schölkopf, Mrinmaya Sachan, and Taylor Berg-Kirkpatrick. 2023 · 2023
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CodexLeaks: Privacy leaks from code generation language models in GitHub copilot
Liang Niu, Shujaat Mirza, Zayd Maradni, and Christina Pöpper. 2023 · 2023
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Can sensitive information be deleted from llms? objectives for defending against extraction attacks
Towards safer large language models through machine unlearning
Zheyuan Liu, Guangyao Dou, Zhaoxuan Tan, Yijun Tian, and Meng Jiang. 2024 · 2024
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Using an llm to help with code understanding
Daye Nam, Andrew Macvean, Vincent Hellendoorn, Bogdan Vasilescu, and Brad Myers. 2024 · 2024
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Undesirable memorization in large language models: A survey
Ali Satvaty, Suzan Verberne, and Fatih Turkmen. 2024 · 2024
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Deepseekmath: Pushing the limits of mathematical reasoning in open language models
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. 2024 · 2024
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Generalization v.s. memorization: Tracing language models’ capabilities back to pretraining data
Xinyi Wang, Antonis Antoniades, Yanai Elazar, Alfonso Amayuelas, Alon Albalak, Kexun Zhang, and William Yang Wang. 2024 · 2024
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Vaidehi Patil, Peter Hase, and Mohit Bansal. 2023 · 2023
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Code llama: Open foundation models for code
Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Romain Sauvestre, Tal Remez, et al. 2023 · 2023
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Bag of tricks for training data extraction from language models
Weichen Yu, Tianyu Pang, Qian Liu, Chao Du, Bingyi Kang, Yan Huang, Min Lin, and Shuicheng Yan. 2023 · 2023
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Zhexin Zhang, Jiaxin Wen, and Minlie Huang. 2023 · 2023
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Do membership inference attacks work on large language models?
Michael Duan, Anshuman Suri, Niloofar Mireshghallah, Sewon Min, Weijia Shi, Luke Zettlemoyer, Yulia Tsvetkov, Yejin Choi, David Evans, and Hannaneh Hajishirzi. 2024 · 2024
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Membership inference attacks against fine-tuned large language models via self-prompt calibration
Wenjie Fu, Huandong Wang, Chen Gao, Guanghua Liu, Yong Li, and Tao Jiang. 2024 · 2024
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The neurIPS 2024 LLM privacy challenge
Qinbin Li, Junyuan Hong, Chulin Xie, Junyi Hou, Yiqun Diao, Zhun Wang, Dan Hendrycks, Zhangyang Wang, Bo Li, Bingsheng He, and Dawn Song. 2024 · 2024
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Not all tokens are what you need for pretraining
Zhenghao Lin, Zhibin Gou, Yeyun Gong, Xiao Liu, Ruochen Xu, Chen Lin, Yujiu Yang, Jian Jiao, Nan Duan, Weizhu Chen, et al. 2024 · 2024
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Memorization and privacy risks in domain-specific large language models
Xinyu Yang, Zichen Wen, Wenjie Qu, Zhaorun Chen, Zhiying Xiang, Beidi Chen, and Huaxiu Yao. 2024 · 2024
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Machine unlearning of pre-trained large language models
Jin Yao, Eli Chien, Minxin Du, Xinyao Niu, Tianhao Wang, Zezhou Cheng, and Xiang Yue. 2024 · 2024
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Exploring memorization in fine-tuned language models
Shenglai Zeng, Yaxin Li, Jie Ren, Yiding Liu, Han Xu, Pengfei He, Yue Xing, Shuaiqiang Wang, Jiliang Tang, and Dawei Yin. 2024 · 2024
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Amazon comprehend
Amazon Web Services, Inc. 2025 · 2025
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Apache license, version 2.0
Apache. 2004 · 2025
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Openrail-m license
BigCode. 2022 · 2025
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Scalable extraction of training data from aligned, production language models
Milad Nasr, Javier Rando, Nicholas Carlini, Jonathan Hayase, Matthew Jagielski, A. Feder Cooper, Daphne Ippolito, Christopher A. Choquette-Choo, Florian Tramèr, and Katherine Lee. 2025 · 2025
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