Order of Magnitude Speedups for LLM Membership Inference

Membership inference attacks from first principles

Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer. 2022 · 1914

Earlier work this paper cites.

Calibrating noise to sensitivity in private data analysis

Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006 · 2006

Earlier work this paper cites.

Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays

Nils Homer, Szabolcs Szelinger, Margot Redman, David Duggan, Waibhav Tembe, Jill Muehling, John V Pearson, Dietrich A Stephan, Stanley F Nelson, and David W Craig. 2008 · 2008

Earlier work this paper cites.

The algorithmic foundations of differential privacy

Cynthia Dwork and Aaron Roth. 2014 · 2014

Earlier work this paper cites.

Character-level convolutional networks for text classification

Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015

Earlier work this paper cites.

Pointer sentinel mixture models

Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016 · 2016

Earlier work this paper cites.

Simple and scalable predictive uncertainty estimation using deep ensembles

Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. 2017 · 2017

Earlier work this paper cites.

Membership inference attacks against machine learning models

Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017 · 2017

Earlier work this paper cites.

Understanding membership inferences on well-generalized learning models

Original

Yunhui Long, Vincent Bindschaedler, Lei Wang, Diyue Bu, Xiaofeng Wang, Haixu Tang, Carl A Gunter, and Kai Chen. 2018 · 2018

Earlier work this paper cites.

Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization

Shashi Narayan, Shay B Cohen, and Mirella Lapata. 2018 · 2018

Cited alongside, same era.

Privacy risk in machine learning: Analyzing the connection to overfitting

Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha. 2018 · 2018

Cited alongside, same era.

White-box vs black-box: Bayes optimal strategies for membership inference

Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, and Hervé Jégou. 2019 · 2019

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.

Revisiting membership inference under realistic assumptions

Bargav Jayaraman, Lingxiao Wang, Katherine Knipmeyer, Quanquan Gu, and David Evans. 2021 · 2021

Cited alongside, same era.

Membership inference attacks against language models via neighbourhood comparison

Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, Bernhard Schoelkopf, Mrinmaya Sachan, and Taylor Berg-Kirkpatrick. 2023 · 2023

Later among the works it cites.

Scalable extraction of training data from (production) language models

Original

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.

Llama: Open and efficient foundation language models

Original

Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023 · 2023

Later among the works it cites.

Counterfactual memorization in neural language models

Chiyuan Zhang, Daphne Ippolito, Katherine Lee, Matthew Jagielski, Florian Tramèr, and Nicholas Carlini. 2023 · 2023

Later among the works it cites.

Scalable membership inference attacks via quantile regression