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Large Language Models (LLMs) represent a significant advancement in artificial intelligence, finding applications across various domains.
What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam D. Smith · 1935
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A mathematical theory of communication
C. E. Shannon · 1948
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Universal Declaration of Human Rights
United Nations · 1948
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Safeguarding cryptographic keys
G. R. Blakley · 1979
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How to share a secret
Adi Shamir · 1979
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Protocols for secure computations
Andrew C. Yao · 1982
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A randomized protocol for signing contracts
Shimon Even, Oded Goldreich, and Abraham Lempel · 1985
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All-or-nothing disclosure of secrets
Gilles Brassard, Claude Crépeau, and Jean-Marc Robert · 1986
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Zero-knowledge proofs of identity
Uriel Feige, Amos Fiat, and Adi Shamir · 1988
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Definitions and properties of zero-knowledge proof systems
Oded Goldreich and Yair Oren · 1994
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Long Short-Term Memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Guaranteeing anonymity when sharing medical data, the datafly system
Latanya Sweeney · 1997
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Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression
Pierangela Samarati and Latanya Sweeney · 1998
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The enron corpus: A new dataset for email classification research
Bryan Klimt and Yiming Yang · 2004
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Privacy as contextual integrity
Helen Nissenbaum · 2004
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How to exchange secrets with oblivious transfer
Michael O. Rabin · 2005
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Pattern Recognition and Machine Learning
Christopher M. Bishop · 2006
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Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
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Copyright Law: A Handbook of Contemporary Research
P. Torremans · 2007
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Strengthening the security of distributed oblivious transfer
Kai Yuen Cheong, Takeshi Koshiba, and Shohei Nishiyama · 2009
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Fully homomorphic encryption using ideal lattices
Craig Gentry · 2009
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Privacy integrated queries: an extensible platform for privacy-preserving data analysis
Frank D. McSherry · 2009
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A multiplicative weights mechanism for privacy-preserving data analysis
Moritz Hardt and Guy N. Rothblum · 2010
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Automatic de-identification of textual documents in the electronic health record: A review of recent research
Stephane Meystre, F Friedlin, Brett South, Shuying Shen, and Matthew Samore · 2010
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Privacy in context: Technology, policy, and the integrity of social life
Helen Nissenbaum · 2010
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Optimal bounds for semi-honest quantum oblivious transfer
Andre Chailloux, Gus Gutoski, and Jamie Sikora · 2013
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Broadening the scope of differential privacy using metrics
Konstantinos Chatzikokolakis, Miguel E. Andrés, Nicolás Emilio Bordenabe, and Catuscia Palamidessi · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, A. Ng, and Christopher Potts · 2013
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Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D. Sarwate · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Rappor: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Aleksandra Korolova, and Vasyl Pihur · 2014
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Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing
Matthew Fredrikson, Eric Lantz, Somesh Jha, Simon Lin, David Page, and Thomas Ristenpart · 2014
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Homomorphic authenticated encryption secure against chosen-ciphertext attack
Chihong Joo and Aaram Yun · 2014
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Modelling and automatically analysing privacy properties for honest-but-curious adversaries
Andrew J. Paverd and Andrew C. Martin · 2014
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Versatile query scrambling for private web search
Avi Arampatzis, George Drosatos, and Pavlos S. Efraimidis · 2015
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Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom · 2015
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User review sites as a resource for large-scale sociolinguistic studies
Dirk Hovy, Anders Johannsen, and Anders Søgaard · 2015
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The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2015
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
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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
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Conversational contextual cues: The case of personalization and history for response ranking, 2016
Rami Al-Rfou, Marc Pickett, Javier Snaider, Yun hsuan Sung, Brian Strope, and Ray Kurzweil · 2016
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Regulation (eu) 2016/679 of the european parliament and of the council, 4 2016
General Data Protection Regulation · 2016
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Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy
Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, and John Wernsing · 2016
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Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Mimic-iii, a freely accessible critical care database
Alistair E.W. Johnson, Tom J. Pollard, Lu Shen, Li-wei H. Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G. Mark · 2016
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Automated anonymization of text documents
Nuno J. Mamede, J. Baptista, and Francisco Dias · 2016
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Federated learning of deep networks using model averaging
H. B. McMahan, Eider Moore, Daniel Ramage, and Blaise Agüera y Arcas · 2016
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Stealing machine learning models via prediction { \{ APIs } \}
Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart · 2016
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Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
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Language model behavior: A comprehensive survey
Tyler A. Chang and Benjamin K. Bergen · 2017
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Collecting telemetry data privately
Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Quora question pairs, 2017
Nikhil Dandekar Lili Jiang, Meg Risdal · 2017
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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2017
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Rényi differential privacy
Ilya Mironov · 2017
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Nist special publication 800-63-3: Digital identity guidelines
National Institute of Standards and Technology · 2017
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Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Learning with privacy at scale differential
Apple Differential Privacy Team · 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
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Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
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Chorus: a programming framework for building scalable differential privacy mechanisms
Noah M. Johnson, Joseph P. Near, Joseph M. Hellerstein, and Dawn Xiaodong Song · 2018
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Incognito: A method for obfuscating web data
Rahat Masood, Dinusha Vatsalan, Muhammad Ikram, and Mohamed Ali Kaafar · 2018
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Learning differentially private recurrent language models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
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Scalable private learning with PATE
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson · 2018
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Algorithms that remember: model inversion attacks and data protection law
Michael Veale, Reuben Binns, and Lilian Edwards · 2018
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Loss functions for multiset prediction
Sean Welleck, Zixin Yao, Yu Gai, Jialin Mao, Zheng Zhang, and Kyunghyun Cho · 2018
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Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
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Ektelo: A framework for defining differentially-private computations
Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, and Gerome Miklau · 2018
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Secure multi-party computation: Theory, practice and applications
Chuan Zhao, Shengnan Zhao, Minghao Zhao, Zhenxiang Chen, Chong-Zhi Gao, Hongwei Li, and Yu an Tan · 2018
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Local differential privacy: a tutorial, 2019
Björn Bebensee · 2019
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Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2019
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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
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On the use of arxiv as a dataset, 2019
Colin B. Clement, Matthew Bierbaum, Kevin P. O’Keeffe, and Alexander A. Alemi · 2019
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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
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Leveraging hierarchical representations for preserving privacy and utility in text
Oluwaseyi Feyisetan, Tom Diethe, and Thomas Drake · 2019
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Openwebtext corpus
Aaron Gokaslan and Vanya Cohen · 2019
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Diffprivlib: The ibm differential privacy library
Naoise Holohan, Stefano Braghin, Pól Mac Aonghusa, and Killian Levacher · 2019
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Parameter-efficient transfer learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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Deep reinforcement learning-based text anonymization against private-attribute inference
Ahmadreza Mosallanezhad, Ghazaleh Beigi, and Huan Liu · 2019
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Natural text anonymization using universal transformer with a self-attention
Aleksandr Romanov, Anna Kurtukova, Anastasia Fedotova, and Roman Meshcheryakov · 2019
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Release strategies and the social impacts of language models, 2019
Irene Solaiman, Miles Brundage, Jack Clark, Amanda Askell, Ariel Herbert-Voss, Jeff Wu, Alec Radford, Gretchen Krueger, Jong Wook Kim, Sarah Kreps, Miles McCain, Alex Newhouse, Jason Blazakis, Kris McGuffie, and Jasmine Wang · 2019
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Auditing data provenance in text-generation models
Congzheng Song and Vitaly Shmatikov · 2019
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A survey on deep learning techniques for privacy-preserving
Harry Chandra Tanuwidjaja, Rakyong Choi, and Kwangjo Kim · 2019
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Paraphrasing with large language models
Sam Witteveen and Martin Andrews · 2019
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Deep leakage from gradients
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
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Towards the alexnet moment for homomorphic encryption: Hcnn, the first homomorphic cnn on encrypted data with gpus
Ahmad Al Badawi, Chao Jin, Jie Lin, Chan Fook Mun, Sim Jun Jie, Benjamin Hong Meng Tan, Xiao Nan, Khin Mi Mi Aung, and Vijay Ramaseshan Chandrasekhar · 2020
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A review of privacy-preserving techniques for deep learning
Amine Boulemtafes, Abdelouahid Derhab, and Yacine Challal · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Does learning require memorization? a short tale about a long tail
Vitaly Feldman · 2020
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What neural networks memorize and why: discovering the long tail via influence estimation
Vitaly Feldman and Chiyuan Zhang · 2020
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The pile: An 800gb dataset of diverse text for language modeling, 2020
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
Earlier work this paper cites.
Inverting gradients - how easy is it to break privacy in federated learning?
Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, and Michael Moeller · 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
Cited alongside, same era.
Differentially private language models benefit from public pre-training
Gavin Kerrigan, Dylan Slack, and Jens Tuyls · 2020
Cited alongside, same era.
Thieves on sesame street! model extraction of bert-based apis
Kalpesh Krishna, Gaurav Singh Tomar, Ankur P. Parikh, Nicolas Papernot, and Mohit Iyyer · 2020
Cited alongside, same era.
Privacy in deep learning: A survey
Fatemehsadat Mirshghallah, Mohammadkazem Taram, Praneeth Vepakomma, Abhishek Singh, Ramesh Raskar, and Hadi Esmaeilzadeh · 2020
Cited alongside, same era.
Ml privacy meter: Aiding regulatory compliance by quantifying the privacy risks of machine learning
Sasi Kumar Murakonda and R. Shokri · 2020
Cited alongside, same era.
Automatic clipping: Differentially private deep learning made easier and stronger
Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, and George Karypis · 2023
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Unlearn what you want to forget: Efficient unlearning for LLMs
Jiaao Chen and Diyi Yang · 2023
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A customized text sanitization mechanism with differential privacy
Sai Chen, Fengran Mo, Yanhao Wang, Cen Chen, Jian-Yun Nie, Chengyu Wang, and Jamie Cui · 2023
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11% of data employees paste into ChatGPT is confidential | Cyberhaven — cyberhaven.com, 2023
Cameron Coles · 2023
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Recent advances of differential privacy in centralized deep learning: A systematic survey
Lea Demelius, Roman Kern, and Andreas Trügler · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Local and central differential privacy for robustness and privacy in federated learning
Mohammad Naseri, Jamie Hayes, and Emiliano De Cristofaro · 2020
Cited alongside, same era.
The opendp white paper
Team OpenDP · 2020
Cited alongside, same era.
Privacy risks of general-purpose language models
Xudong Pan, Mi Zhang, Shouling Ji, and Min Yang · 2020
Cited alongside, same era.
Making the shoe fit: Architectures, initializations, and tuning for learning with privacy, 2020
Nicolas Papernot, Steve Chien, Shuang Song, Abhradeep Thakurta, and Ulfar Erlingsson · 2020
Cited alongside, same era.
Training production language models without memorizing user data, 2020
Swaroop Ramaswamy, Om Thakkar, Rajiv Mathews, Galen Andrew, H. Brendan McMahan, and Françoise Beaufays · 2020
Cited alongside, same era.
A survey of privacy attacks in machine learning
Maria Rigaki and Sebastián García · 2020
Cited alongside, same era.
Few-shot text generation with natural language instructions
Timo Schick and Hinrich Schütze · 2020
Cited alongside, same era.
Erik Derner, Kristina Batistic, Jan Zah’alka, and Robert Babuka · 2023
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Context-aware differential privacy for language modeling
My-Hoa Nathalie Dinh and Ferdinando Fioretto · 2023
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Differentially private diffusion models
Tim Dockhorn, Tianshi Cao, Arash Vahdat, and Karsten Kreis · 2023
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Sanitizing sentence embeddings (and labels) for local differential privacy
Minxin Du, Xiang Yue, Sherman S. M. Chow, and Huan Sun · 2023
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Dp-forward: Fine-tuning and inference on language models with differential privacy in forward pass
Minxin Du, Xiang Yue, Sherman S. M. Chow, Tianhao Wang, Chenyu Huang, and Huan Sun · 2023
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On the privacy risk of in-context learning
Haonan Duan, Adam Dziedzic, Mohammad Yaghini, Nicolas Papernot, and Franziska Boenisch · 2023
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Why is public pretraining necessary for private model training?
Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Guha Thakurta, and Lun Wang · 2023
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Lessons learned: Surveying the practicality of differential privacy in the industry
Gonzalo Munilla Garrido, Xiaoyuan Liu, Floria Matthes, and Dawn Song · 2023
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Differentially private diffusion models generate useful synthetic images
Sahra Ghalebikesabi, Leonard Berrada, Sven Gowal, Ira Ktena, Robert Stanforth, Jamie Hayes, Soham De, Samuel L. Smith, Olivia Wiles, and Borja Balle · 2023
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Privacy-Preserving Machine Learning for Healthcare: Open Challenges and Future Perspectives , pp. 25–40
Alejandro Guerra-Manzanares, L. Julian Lechuga Lopez, Michail Maniatakos, and Farah E. Shamout · 2023
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Large language models: A comprehensive survey of its applications, challenges, limitations, and future prospects
Muhammad Usman Hadi, al tashi, Rizwan Qureshi, Abbas Shah, Muhammad Irfan, Anas Zafar, Muhammad Bilal Shaikh, Naveed Akhtar, Jia Wu, Seyedali Mirjalili, Qasem Al-Tashi, Amgad Muneer, Mohammed Ali Al-garadi, Gru Cnn, and T5 RoBERTa · 2023
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Security and privacy issues of federated learning
Jahid Hasan · 2023
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Ciphergpt: Secure two-party gpt inference
Xiaoyang Hou, Jian Liu, Jingyu Li, Yu hai Li, and Cheng Hong · 2023
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DP-BART for privatized text rewriting under local differential privacy
Timour Igamberdiev and Ivan Habernal · 2023
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Knowledge sanitization of large language models
Yoichi Ishibashi and Hidetoshi Shimodaira · 2023
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Copyright violations and large language models
Antonia Karamolegkou, Jiaang Li, Li Zhou, and Anders Søgaard · 2023
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Preserving privacy through dememorization: An unlearning technique for mitigating memorization risks in language models
Aly M. Kassem, Omar Mahmoud, and Sherif Saad · 2023
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Amazon warns employees not to share confidential information with ChatGPT after seeing cases where its answer ’closely matches existing material’ from inside the company, 1 2023
Eugene Kim · 2023
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Propile: Probing privacy leakage in large language models
Siwon Kim, Sangdoo Yun, Hwaran Lee, Martin Gubri, Sungroh Yoon, and Seong Joon Oh · 2023
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Multi-step jailbreaking privacy attacks on ChatGPT
Haoran Li, Dadi Guo, Wei Fan, Mingshi Xu, Jie Huang, Fanpu Meng, and Yangqiu Song · 2023
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Sentence embedding leaks more information than you expect: Generative embedding inversion attack to recover the whole sentence
Haoran Li, Mingshi Xu, and Yangqiu Song · 2023
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Italy orders ChatGPT blocked citing data protection concerns | TechCrunch — techcrunch.com
Natasha Lomas · 2023
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Analyzing leakage of personally identifiable information in language models
Nils Lukas, A. Salem, Robert Sim, Shruti Tople, Lukas Wutschitz, and Santiago Zanella-B’eguelin · 2023
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Membership inference attacks against language models via neighbourhood comparison
Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, Bernhard Schoelkopf, Mrinmaya Sachan, and Taylor Berg-Kirkpatrick · 2023
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How much do language models copy from their training data? evaluating linguistic novelty in text generation using RAVEN
R. Thomas McCoy, Paul Smolensky, Tal Linzen, Jianfeng Gao, and Asli Celikyilmaz · 2023
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Mass-editing memory in a transformer
Kevin Meng, Arnab Sen Sharma, Alex J Andonian, Yonatan Belinkov, and David Bau · 2023
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Samsung Fab Data Leak: How ChatGPT Exposed Sensitive Information — electropages.com
Robin Mitchell · 2023
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Text embeddings reveal (almost) as much as text
John Morris, Volodymyr Kuleshov, Vitaly Shmatikov, and Alexander Rush · 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
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Federated learning and differential privacy in clinical health: Extensive survey
David Odera · 2023
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March 20 chatgpt outage: Here’s what happened: an update on our findings, the actions we’ve taken, and technical details of the bug, 3 2023
OpenAI · 2023
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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
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How to dp-fy ml: A practical guide to machine learning with differential privacy
Natalia Ponomareva, Hussein Hazimeh, Alexey Kurakin, Zheng Xu, Carson E. Denison, H. B. McMahan, Sergei Vassilvitskii, Steve Chien, and Abhradeep Thakurta · 2023
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Aman Priyanshu, Supriti Vijay, Ayush Kumar, Rakshit Naidu, and Fatemehsadat Mireshghallah · 2023
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Towards benchmarking privacy risk for differential privacy: A survey
Dmitry Prokhorenkov and Yang Cao · 2023
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Knowledge unlearning for llms: Tasks, methods, and challenges, 2023
Nianwen Si, Hao Zhang, Heyu Chang, Wenlin Zhang, Dan Qu, and Weiqiang Zhang · 2023
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Joint prompt optimization of stacked LLMs using variational inference
Alessandro Sordoni, Xingdi Yuan, Marc-Alexandre Côté, Matheus Pereira, Adam Trischler, Ziang Xiao, Arian Hosseini, Friederike Niedtner, and Nicolas Le Roux · 2023
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How to keep text private? a systematic review of deep learning methods for privacy-preserving natural language processing
Samuel Sousa and Roman Kern · 2023
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Detecting personal information in training corpora: an analysis
Nishant Subramani, Sasha Luccioni, Jesse Dodge, and Margaret Mitchell · 2023
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Xianghui Sun, Yunjie Ji, Baochang Ma, and Xiangang Li · 2023
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Suryakiran at mediqa-sum 2023: Leveraging lora for clinical dialogue summarization, 2023
Kunal Suri, Prakhar Mishra, Saumajit Saha, and Atul Singh · 2023
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Llama 2: Open foundation and fine-tuned chat models, 2023
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom · 2023
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Analytical composition of differential privacy via the edgeworth accountant, 2023
Hua Wang, Sheng Gao, Huanyu Zhang, Milan Shen, and Weijie J Su · 2023
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DEPN: Detecting and editing privacy neurons in pretrained language models
Xinwei Wu, Junzhuo Li, Minghui Xu, Weilong Dong, Shuangzhi Wu, Chao Bian, and Deyi Xiong · 2023
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Federated learning of gboard language models with differential privacy
Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher Choquette, Peter Kairouz, Brendan Mcmahan, Jesse Rosenstock, and Yuanbo Zhang · 2023
Later among the works it cites.
Randomized quantization is all you need for differential privacy in federated learning
Yeojoon Youn, Zihao Hu, Juba Ziani, and Jacob Abernethy · 2023
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Synthetic text generation with differential privacy: A simple and practical recipe
Xiang Yue, Huseyin Inan, Xuechen Li, Girish Kumar, Julia McAnallen, Hoda Shajari, Huan Sun, David Levitan, and Robert Sim · 2023
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A systematic survey for differential privacy techniques in federated learning
Yi Zhang, Yunfan Lu, and Fengxia Liu · 2023
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ETHICIST: Targeted training data extraction through loss smoothed soft prompting and calibrated confidence estimation
Zhexin Zhang, Jiaxin Wen, and Minlie Huang · 2023
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A survey of large language models, 2023
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, and Ji-Rong Wen · 2023
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Primer: Fast private transformer inference on encrypted data
Mengxin Zheng, Qian Lou, and Lei Jiang · 2023
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On the efficacy of group-wise clipping in differentially private optimization, 2024
Zhiqi Bu, Ruixuan Liu, Yu-Xiang Wang, Sheng Zha, and George Karypis · 2024
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Stealing part of a production language model
Nicholas Carlini, Daniel Paleka, Krishnamurthy Dj Dvijotham, Thomas Steinke, Jonathan Hayase, A. Feder Cooper, Katherine Lee, Matthew Jagielski, Milad Nasr, Arthur Conmy, Eric Wallace, David Rolnick, and Florian Tramèr · 2024
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Synthetic query generation for privacy-preserving deep retrieval systems using differentially private language models
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Text embedding inversion security for multilingual language models
Yiyi Chen, Heather Lent, and Johannes Bjerva · 2024
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Reconstruct your previous conversations! comprehensively investigating privacy leakage risks in conversations with GPT models
Junjie Chu, Zeyang Sha, Michael Backes, and Yang Zhang · 2024
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Delving into differentially private transformer
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Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
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Who’s harry potter? approximate unlearning for LLMs, 2024
Ronen Eldan and Mark Russinovich · 2024
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Logits of API-protected LLMs leak proprietary information
Matthew Finlayson, Xiang Ren, and Swabha Swayamdipta · 2024
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OLMo: Accelerating the science of language models
Dirk Groeneveld, Iz Beltagy, Evan Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, Ananya Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, Shane Arora, David Atkinson, Russell Authur, Khyathi Chandu, Arman Cohan, Jennifer Dumas, Yanai Elazar, Yuling Gu, Jack Hessel, Tushar Khot, William Merrill, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew Peters, Valentina Pyatkin, Abhilasha Ravichander, Dustin Schwenk, Saurabh Shah, William Smith, Emma Strubell, Nishant Subramani, Mitchell Wortsman, Pradeep Dasigi, Nathan Lambert, Kyle Richardson, Luke Zettlemoyer, Jesse Dodge, Kyle Lo, Luca Soldaini, Noah Smith, and Hannaneh Hajishirzi · 2024
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DP-OPT: Make large language model your privacy-preserving prompt engineer
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DP-NMT: Scalable differentially private machine translation
Timour Igamberdiev, Doan Nam Long Vu, Felix Kuennecke, Zhuo Yu, Jannik Holmer, and Ivan Habernal · 2024
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2024
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Harnessing large-language models to generate private synthetic text, 2024
Alexey Kurakin, Natalia Ponomareva, Umar Syed, Liam MacDermed, and Andreas Terzis · 2024
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Split-and-denoise: Protect large language model inference with local differential privacy
Peihua Mai, Ran Yan, Zhe Huang, Youjia Yang, and Yan Pang · 2024
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Large language models: A survey, 2024
Shervin Minaee, Tomas Mikolov, Narjes Nikzad, Meysam Chenaghlu, Richard Socher, Xavier Amatriain, and Jianfeng Gao · 2024
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Can LLMs keep a secret? testing privacy implications of language models via contextual integrity theory
Niloofar Mireshghallah, Hyunwoo Kim, Xuhui Zhou, Yulia Tsvetkov, Maarten Sap, Reza Shokri, and Yejin Choi · 2024
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Language model inversion
John Xavier Morris, Wenting Zhao, Justin T Chiu, Vitaly Shmatikov, and Alexander M Rush · 2024
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Bolt: Privacy-preserving, accurate and efficient inference for transformers
Qi Pang, Jinhao Zhu, Helen Möllering, Wenting Zheng, and Thomas Schneider · 2024
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Can sensitive information be deleted from LLMs? objectives for defending against extraction attacks
Vaidehi Patil, Peter Hase, and Mohit Bansal · 2024
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In-context unlearning: Language models as few shot unlearners, 2024
Martin Pawelczyk, Seth Neel, and Himabindu Lakkaraju · 2024
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The fineweb datasets: Decanting the web for the finest text data at scale
Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, Anton Lozhkov, Margaret Mitchell, Colin Raffel, Leandro Von Werra, and Thomas Wolf · 2024
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Encryption-friendly llm architecture, 2024
Donghwan Rho, Taeseong Kim, Minje Park, Jung Woo Kim, Hyunsik Chae, Jung Hee Cheon, and Ernest K. Ryu · 2024
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Exploring the landscape of machine unlearning: A comprehensive survey and taxonomy
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Quantifying association capabilities of large language models and its implications on privacy leakage
Hanyin Shao, Jie Huang, Shen Zheng, and Kevin Chang · 2024
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" do anything now": Characterizing and evaluating in-the-wild jailbreak prompts on large language models
Xinyue Shen, Zeyuan Chen, Michael Backes, Yun Shen, and Yang Zhang · 2024
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Detecting pretraining data from large language models
Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, and Luke Zettlemoyer · 2024
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Dolma: an open corpus of three trillion tokens for language model pretraining research
Luca Soldaini, Rodney Kinney, Akshita Bhagia, Dustin Schwenk, David Atkinson, Russell Authur, Ben Bogin, Khyathi Chandu, Jennifer Dumas, Yanai Elazar, Valentin Hofmann, Ananya Jha, Sachin Kumar, Li Lucy, Xinxi Lyu, Nathan Lambert, Ian Magnusson, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew Peters, Abhilasha Ravichander, Kyle Richardson, Zejiang Shen, Emma Strubell, Nishant Subramani, Oyvind Tafjord, Evan Walsh, Luke Zettlemoyer, Noah Smith, Hannaneh Hajishirzi, Iz Beltagy, Dirk Groeneveld, Jesse Dodge, and Kyle Lo · 2024
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Privacy-preserving in-context learning with differentially private few-shot generation
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Gemini: A family of highly capable multimodal models, 2024
Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M. Dai, Anja Hauth, Katie Millican, David Silver, Melvin Johnson, Ioannis Antonoglou, Julian Schrittwieser, Amelia Glaese, Jilin Chen, Emily Pitler, Timothy Lillicrap, Angeliki Lazaridou, Orhan Firat, James Molloy, Michael Isard, Paul R. 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