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Large language models (LLMs) require a significant redesign in solutions to preserve privacy in data-intensive applications due to their text-generation capabilities.
The enron corpus: A new dataset for email classification research
Bryan Klimt and Yiming Yang. 2004 · 2004
Earlier work this paper cites.
Privacy by design: The 7 foundational principles
Ann Cavoukian et al. 2009 · 2009
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Common crawl – building an open web-scale crawl using hadoop
Ahad Rana. 2010 · 2010
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Visual cryptography for biometric privacy
Arun Ross and Asem Othman. 2010 · 2010
Earlier work this paper cites.
Privacy by design
Peter Schaar. 2010 · 2010
Earlier work this paper cites.
Hcpp: Cryptography based secure ehr system for patient privacy and emergency healthcare
Jinyuan Sun, Xiaoyan Zhu, Chi Zhang, and Yuguang Fang. 2011 · 2011
Earlier work this paper cites.
Privacy by design in the age of big data
Ann Cavoukian and Jeff Jonas. 2012 · 2012
Earlier work this paper cites.
The challenges of privacy by design
Sarah Spiekermann. 2012 · 2012
Earlier work this paper cites.
Privacy protection in biometric-based recognition systems: A marriage between cryptography and signal processing
Mauro Barni, Giulia Droandi, and Riccardo Lazzeretti. 2015 · 2015
Earlier work this paper cites.
The LAMBADA dataset: Word prediction requiring a broad discourse context
Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Ngoc Quan Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, and Raquel Fernández. 2016 · 2016
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Investigation of cryptography algorithms used for security and privacy protection in smart grid
Omar G Abood, Mahmoud A Elsadd, and Shawkat K Guirguis. 2017 · 2017
Earlier work this paper cites.
The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song. 2019 · 2019
Earlier work this paper cites.
The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, and Connor Leahy. 2020 · 2020
Cited alongside, same era.
GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow
Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman. 2021 · 2021
Cited alongside, same era.
Editing factual knowledge in language models
Nicola De Cao, Wilker Aziz, and Ivan Titov. 2021 · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Transformer feed-forward layers are key-value memories
Mor Geva, Roei Schuster, Jonathan Berant, and Omer Levy. 2021 · 2021
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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Scalable extraction of training data from (production) language models
Milad Nasr, Nicholas Carlini, Jonathan Hayase, Matthew Jagielski, A Feder Cooper, Daphne Ippolito, Christopher A Choquette-Choo, Eric Wallace, Florian Tramèr, and Katherine Lee. 2023 · 2023
Later among the works it cites.
Controlling the extraction of memorized data from large language models via prompt-tuning
Mustafa Ozdayi, Charith Peris, Jack FitzGerald, Christophe Dupuy, Jimit Majmudar, Haidar Khan, Rahil Parikh, and Rahul Gupta. 2023 · 2023
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Can sensitive information be deleted from llms? objectives for defending against extraction attacks
Vaidehi Patil, Peter Hase, and Mohit Bansal. 2023 · 2023
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Cited alongside, same era.
GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Ben Wang and Aran Komatsuzaki. 2021 · 2021
Cited alongside, same era.
Gpt-neox-20b: An open-source autoregressive language model
Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, and Samuel Weinbach. 2022 · 2022
Cited alongside, same era.
What does it mean for a language model to preserve privacy?
Hannah Brown, Katherine Lee, Fatemehsadat Mireshghallah, Reza Shokri, and Florian Tramèr. 2022 · 2022
Cited alongside, same era.
Memory-based model editing at scale
Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D. Manning, and Chelsea Finn. 2022 · 2022
Cited alongside, same era.
Scaling language models: Methods, analysis & insights from training gopher
Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan, Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen Huang, Amelia Glaese, Johannes Welbl, Sumanth Dathathri, Saffron Huang, Jonathan Uesato, John Mellor, Irina Higgins, Antonia Creswell, Nat McAleese, Amy Wu, Erich Elsen, Siddhant Jayakumar, Elena Buchatskaya, David Budden, Esme Sutherland, Karen Simonyan, Michela Paganini, Laurent Sifre, Lena Martens, Xiang Lorraine Li, Adhiguna Kuncoro, Aida Nematzadeh, Elena Gribovskaya, Domenic Donato, Angeliki Lazaridou, Arthur Mensch, Jean-Baptiste Lespiau, Maria Tsimpoukelli, Nikolai Grigorev, Doug Fritz, Thibault Sottiaux, Mantas Pajarskas, Toby Pohlen, Zhitao Gong, Daniel Toyama, Cyprien de Masson d’Autume, Yujia Li, Tayfun Terzi, Vladimir Mikulik, Igor Babuschkin, Aidan Clark, Diego de Las Casas, Aurelia Guy, Chris Jones, James Bradbury, Matthew Johnson, Blake Hechtman, Laura Weidinger, Iason Gabriel, William Isaac, Ed Lockhart, Simon Osindero, Laura Rimell, Chris Dyer, Oriol Vinyals, Kareem Ayoub, Jeff Stanway, Lorrayne Bennett, Demis Hassabis, Koray Kavukcuoglu, and Geoffrey Irving. 2022 · 2022
Cited alongside, same era.
Pythia: A suite for analyzing large language models across training and scaling
Stella Biderman, Hailey Schoelkopf, Quentin Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, and Oskar van der Wal. 2023 · 2023
Cited alongside, same era.
Rebuilding rome : Resolving model collapse during sequential model editing
Akshat Gupta, Sidharth Baskaran, and Gopala Anumanchipalli. 2024a
Cited in the paper.
Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, and Ningyu Zhang. 2023 · 2023
Later among the works it cites.
WilKE: Wise-layer knowledge editor for lifelong knowledge editing
Chenhui Hu, Pengfei Cao, Yubo Chen, Kang Liu, and Jun Zhao. 2024 · 2024
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Rethinking machine unlearning for large language models
Sijia Liu, Yuanshun Yao, Jinghan Jia, Stephen Casper, Nathalie Baracaldo, Peter Hase, Yuguang Yao, Chris Yuhao Liu, Xiaojun Xu, Hang Li, Kush R. Varshney, Mohit Bansal, Sanmi Koyejo, and Yang Liu. 2024 · 2024
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Decodingtrust: A comprehensive assessment of trustworthiness in gpt models
Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, and Bo Li. 2024 · 2024
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The butterfly effect of model editing: Few edits can trigger large language models collapse
Wanli Yang, Fei Sun, Xinyu Ma, Xun Liu, Dawei Yin, and Xueqi Cheng. 2024 · 2024
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Large language model unlearning
Yuanshun Yao, Xiaojun Xu, and Yang Liu. 2024 · 2024
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Are large pre-trained language models leaking your personal information?
Jie Huang, Hanyin Shao, and Kevin Chen-Chuan Chang. 2022 · 2047
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