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Recently, diffusion models have become popular tools for image synthesis because of their high-quality outputs.
Bagging predictors
L. Breiman · 1996
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Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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Auditing differentially private machine learning: How private is private sgd?
M. Jagielski, J. Ullman, and A. Oprea · 2006
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Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays
N. Homer, S. Szelinger, M. Redman, D. Duggan, W. Tembe, J. Muehling, J. V. Pearson, D. A. Stephan, S. F. Nelson, and D. W. Craig · 2008
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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An analysis of single-layer networks in unsupervised feature learning
A. Coates, A. Ng, and H. Lee · 2011
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The algorithmic foundations of differential privacy
C. Dwork and A. Roth · 2014
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Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
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Privacy risk in machine learning: Analyzing the connection to overfitting
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha · 2018
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Logan: Membership inference attacks against generative models
J. Hayes, L. Melis, G. Danezis, and E. De Cristofaro · 2019
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Evaluating differentially private machine learning in practice
B. Jayaraman and D. Evans · 2019
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Generative modeling by estimating gradients of the data distribution
Y. Song and S. Ermon · 2019
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Efficient privacy-preserving nonconvex optimization
L. Wang, B. Jayaraman, D. Evans, and Q. Gu · 2019
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Extracting training data from large language models
N. Carlini, F. Tramer, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, A. Roberts, T. Brown, D. Song, U. Erlingsson, A. Oprea, and C. Raffel · 2020
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Synthetic data - anonymisation groundhog day
T. Stadler, B. Oprisanu, and C. Troncoso · 2022
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Debugging differential privacy: A case study for privacy auditing
F. Tramèr, A. Terzis, T. Steinke, S. Song, M. Jagielski, and N. Carlini · 2022
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Scalable membership inference attacks via quantile regression
M. A. Bertran, S. Tang, A. Roth, M. Kearns, J. Morgenstern, and S. Wu · 2023
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Extracting training data from diffusion models
N. Carlini, J. Hayes, M. Nasr, M. Jagielski, V. Sehwag, F. Tramer, B. Balle, D. Ippolito, and E. Wallace · 2023
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Are diffusion models vulnerable to membership inference attacks?
J. Duan, F. Kong, S. Wang, X. Shi, and K. Xu · 2023
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J. Ho, A. Jain, and P. Abbeel · 2020
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Revisiting membership inference under realistic assumptions
B. Jayaraman, L. Wang, D. Evans, and Q. Gu · 2020
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Adversary instantiation: Lower bounds for differentially private machine learning
M. Nasr, S. Song, A. Thakurta, N. Papernot, and N. Carlini · 2021
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Membership inference attacks from first principles
N. Carlini, S. Chien, M. Nasr, S. Song, A. Terzis, and F. Tramèr · 2022
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M. Nasr, J. Hayes, T. Steinke, B. Balle, F. Tramèr, M. Jagielski, N. Carlini, and A. Terzis · 2023
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White-box membership inference attacks against diffusion models
Y. Pang, T. Wang, X. Kang, M. Huai, and Y. Zhang · 2023
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Membership inference attacks against synthetic data through overfitting detection
B. van Breugel, H. Sun, Z. Qian, and M. van der Schaar · 2023
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