Fetching the paper…
Reading the bibliography…
Machine learning models trained on private datasets have been shown to leak their private data.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Smooth sensitivity and sampling in private data analysis
Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2007
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
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Knock knock, who’s there? membership inference on aggregate location data
Apostolos Pyrgelis, Carmela Troncoso, and Emiliano De Cristofaro · 2017
Earlier work this paper cites.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Earlier work this paper cites.
Comprehensive privacy analysis of deep learning
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2018
Earlier work this paper cites.
Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
Earlier work this paper cites.
Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov · 2019
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
Earlier work this paper cites.
Imagededup
Tanuj Jain, Christopher Lennan, Zubin John, and Dat Tran · 2019
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
Cited alongside, same era.
Machine unlearning: Linear filtration for logit-based classifiers
Thomas Baumhauer, Pascal Schöttle, and Matthias Zeppelzauer · 2020
Cited alongside, same era.
Does learning require memorization? a short tale about a long tail
Vitaly Feldman · 2020
Cited alongside, same era.
What neural networks memorize and why: Discovering the long tail via influence estimation
Vitaly Feldman and Chiyuan Zhang · 2020
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
Later among the works it cites.
Label-only membership inference attacks
Christopher A Choquette-Choo, Florian Tramer, Nicholas Carlini, and Nicolas Papernot · 2021
Later among the works it cites.
Encodermi: Membership inference against pre-trained encoders in contrastive learning
Hongbin Liu, Jinyuan Jia, Wenjie Qu, and Neil Zhenqiang Gong · 2021
Later among the works it cites.
Pervasive label errors in test sets destabilize machine learning benchmarks
Curtis G Northcutt, Anish Athalye, and Jonas Mueller · 2021
Later among the works it cites.
Chasing your long tails: Differentially private prediction in health care settings
Vinith M Suriyakumar, Nicolas Papernot, Anna Goldenberg, and Marzyeh Ghassemi · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Laura Graves, Vineel Nagisetty, and Vijay Ganesh · 2020
Cited alongside, same era.
Auditing differentially private machine learning: How private is private sgd?
Matthew Jagielski, Jonathan Ullman, and Alina Oprea · 2020
Cited alongside, same era.
Characterizing structural regularities of labeled data in overparameterized models
Ziheng Jiang, Chiyuan Zhang, Kunal Talwar, and Michael C Mozer · 2020
Cited alongside, same era.
Stolen memories: Leveraging model memorization for calibrated { \{ White-Box } \} membership inference
Klas Leino and Matt Fredrikson · 2020
Cited alongside, same era.
ML privacy meter: Aiding regulatory compliance by quantifying the privacy risks of machine learning
Sasi Kumar Murakonda and Reza Shokri · 2020
Cited alongside, same era.
Differentially private learning needs better features (or much more data)
Florian Tramer and Dan Boneh · 2020
Cited alongside, same era.
When is memorization of irrelevant training data necessary for high-accuracy learning?
Gavin Brown, Mark Bun, Vitaly Feldman, Adam Smith, and Kunal Talwar · 2021
Cited alongside, same era.
Understanding deep learning (still) requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2021
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 · 2021
Later among the works it cites.
Membership inference attacks against recommender systems
Minxing Zhang, Zhaochun Ren, Zihan Wang, Pengjie Ren, Zhunmin Chen, Pengfei Hu, and Yang Zhang · 2021
Later among the works it cites.
Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer · 2022
Closest in time.
Datamodels: Predicting predictions from training data
Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, and Aleksander Madry · 2022
Closest in time.
Unrolling sgd: Understanding factors influencing machine unlearning
Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, and Nicolas Papernot · 2022
Closest in time.
Truth serum: Poisoning machine learning models to reveal their secrets
Florian Tramèr, Reza Shokri, Ayrton San Joaquin, Hoang Le, Matthew Jagielski, Sanghyun Hong, and Nicholas Carlini · 2022
Closest in time.