Membership Inference Attacks on Large-Scale Models: A Survey

Communication without words. In Communication Theory

Albert Mehrabian. 1968 · 1968

Earlier work this paper cites.

Label-Only Membership Inference Attacks. In Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 139) , Marina Meila and Tong Zhang (Eds.). PMLR, 1964–1974

Christopher A. Choquette-Choo, Florian Tramer, Nicholas Carlini, and Nicolas Papernot. 2021 · 1974

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.

Covering code behavior on input validation in functional testing

Hui Liu and Hee Beng Kuan Tan. 2009 · 2008

Earlier work this paper cites.

ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition . 248–255

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009 · 2009

Earlier work this paper cites.

Training language models to follow instructions with human feedback. In Proceedings of the 36th International Conference on Neural Information Processing Systems (New Orleans, LA, USA) (NIPS ’22) . Curran Associates Inc., Red Hook, NY, USA, Article 2011, 15 pages

Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022 · 2011

Earlier work this paper cites.

Black Box and White Box Testing Techniques - A Literature Review

S. Nidhra. 2012 · 2012

Earlier work this paper cites.

Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (Denver, Colorado, USA) (CCS ’15) . Association for Computing Machinery, New York, NY, USA, 1322–1333

Matt Fredrikson, Somesh Jha, and Thomas Ristenpart. 2015 · 2015

Earlier work this paper cites.

Membership Inference Attacks Against Machine Learning Models. In 2017 IEEE Symposium on Security and Privacy (SP) . 3–18

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

Earlier work this paper cites.

Attention is All you Need. In Advances in Neural Information Processing Systems , I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017

Earlier work this paper cites.

Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha. 2017 · 2017

Earlier work this paper cites.

Multimodal Machine Learning: A Survey and Taxonomy

Tadas Baltrusaitis, Chaitanya Ahuja, and Louis-Philippe Morency. 2019 · 2018

Earlier work this paper cites.

Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security . Association for Computing Machinery, New York, NY, USA, 619–633

Karan Ganju, Qi Wang, Wei Yang, Carl A. Gunter, and Nikita Borisov. 2018 · 2018

Earlier work this paper cites.

Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting . In 2018 IEEE 31st Computer Security Foundations Symposium (CSF) . IEEE Computer Society, Los Alamitos, CA, USA, 268–282

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

Earlier work this paper cites.

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers) , Jill Burstein, Christy Doran, and Thamar Solorio (Eds.). Association for Computational Linguistics, 4171–4186

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019

Earlier work this paper cites.

Language models are unsupervised multitask learners

Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019

Earlier work this paper cites.

What neural networks memorize and why: discovering the long tail via influence estimation. In Proceedings of the 34th International Conference on Neural Information Processing Systems (Vancouver, BC, Canada) (NIPS ’20) . Curran Associates Inc., Red Hook, NY, USA, Article 242, 11 pages

Vitaly Feldman and Chiyuan Zhang. 2020 · 2020

Earlier work this paper cites.

Privacy enhanced multimodal neural representations for emotion recognition. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 34. 7985–7993

Mimansa Jaiswal and Emily Mower Provost. 2020 · 2020

Earlier work this paper cites.

Retrieval-augmented generation for knowledge-intensive NLP tasks. In Proceedings of the 34th International Conference on Neural Information Processing Systems (Vancouver, BC, Canada) (NIPS ’20) . Curran Associates Inc., Red Hook, NY, USA, Article 793, 16 pages

Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020 · 2020

Earlier work this paper cites.

Learning to summarize from human feedback. In Proceedings of the 34th International Conference on Neural Information Processing Systems (Vancouver, BC, Canada) (NIPS ’20) . Curran Associates Inc., Red Hook, NY, USA, Article 253, 14 pages

Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul Christiano. 2020 · 2020

Earlier work this paper cites.

Extracting Training Data from Large Language Models. In 30th USENIX Security Symposium (USENIX Security 21) . USENIX Association, 2633–2650

Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, and Colin Raffel. 2021 · 2021

Earlier work this paper cites.

Emerging trends: A gentle introduction to fine-tuning

Kenneth Ward Church, Zeyu Chen, and Yanjun Ma. 2021 · 2021

Earlier work this paper cites.

Membership Inference Attack Susceptibility of Clinical Language Models

Original

Abhyuday Jagannatha, Bhanu Pratap Singh Rawat, and Hong Yu. 2021 · 2021

Earlier work this paper cites.

Learning Transferable Visual Models From Natural Language Supervision. In International Conference on Machine Learning

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021 · 2021

Cited alongside, same era.

Membership Inference Attacks From First Principles. In 2022 IEEE Symposium on Security and Privacy (SP) . 1897–1914

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

Cited alongside, same era.

Membership Inference Attacks From First Principles. In 2022 IEEE Symposium on Security and Privacy (SP) . 1897–1914

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

Cited alongside, same era.

Membership Inference Attacks on Machine Learning: A Survey

Hongsheng Hu, Zoran Salcic, Lichao Sun, Gillian Dobbie, Philip S. Yu, and Xuyun Zhang. 2022a · 2022

Cited alongside, same era.

Privacy preservation and security challenges: a new frontier multimodal machine learning research

A Survey on Security and Privacy of Large Multimodal Deep Learning Models: Teaching and Learning Perspective. In 2024 21st Learning and Technology Conference (L & T) . 13–18

Md. Abdur Rahman, Lamyaa Alqahtani, Amna Albooq, and Alaa Ainousah. 2024 · 2024

Later among the works it cites.

Self-Comparison for Dataset-Level Membership Inference in Large (Vision-)Language Models

Original

Jie Ren, Kangrui Chen, Chen Chen, Vikash Sehwag, Yue Xing, Jiliang Tang, and Lingjuan Lyu. 2024 · 2024

Later among the works it cites.

Detecting Pretraining Data from Large Language Models. In The Twelfth International Conference on Learning Representations

Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, and Luke Zettlemoyer. 2024 · 2024

Later among the works it cites.

Not All Tokens Are Equal: Membership Inference Attacks Against Fine-tuned Language Models. In 2024 Annual Computer Security Applications Conference (ACSAC) . 31–45

Changtian Song, Dongdong Zhao, and Jianwen Xiang. 2024 · 2024

Later among the works it cites.