Fetching the paper…
Reading the bibliography…
This paper studies defense mechanisms against model inversion (MI) attacks -- a type of privacy attacks aimed at inferring information about the training data distribution given the access to a target machine learning model.
Adversarial neural network inversion via auxiliary knowledge alignment
Yang, Z.; Chang, E.-C.; and Liang, Z. 2019 · 1902
Earlier work this paper cites.
Updates-leak: Data set inference and reconstruction attacks in online learning
Salem, A.; Bhattacharya, A.; Backes, M.; Fritz, M.; and Zhang, Y. 2019 · 1904
Earlier work this paper cites.
The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks
Zhang, Y.; Jia, R.; Pei, H.; Wang, W.; Li, B.; and Song, D. 2019 · 1911
Earlier work this paper cites.
Know you at one glance: A compact vector representation for low-shot learning
Cheng, Y.; Zhao, J.; Wang, Z.; Xu, Y.; Jayashree, K.; Shen, S.; and Feng, J. 2017 · 1932
Earlier work this paper cites.
Induction of decision trees
Quinlan, J. R. 1986 · 1986
Earlier work this paper cites.
Elements of information theory
Cover, T. M. 1999 · 1999
Earlier work this paper cites.
Defending Model Inversion and Membership Inference Attacks via Prediction Purification
Yang, Z.; Shao, B.; Xuan, B.; Chang, E.-C.; and Zhang, F. 2020 · 2005
Earlier work this paper cites.
On entropy approximation for Gaussian mixture random vectors
Huber, M. F.; Bailey, T.; Durrant-Whyte, H.; and Hanebeck, U. D. 2008 · 2008
Earlier work this paper cites.
Data mining with differential privacy
Friedman, A.; and Schuster, A. 2010 · 2010
Cited alongside, same era.
The algorithmic foundations of differential privacy
Dwork, C.; Roth, A.; et al. 2014 · 2014
Cited alongside, same era.
Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing
Fredrikson, M.; Lantz, E.; Jha, S.; Lin, S.; Page, D.; and Ristenpart, T. 2014 · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, K.; and Zisserman, A. 2014 · 2014
Cited alongside, same era.
Generalization in adaptive data analysis and holdout reuse
Dwork, C.; Feldman, V.; Hardt, M.; Pitassi, T.; Reingold, O.; and Roth, A. 2015 · 2015
Cited alongside, same era.
Model inversion attacks that exploit confidence information and basic countermeasures
Ms-celeb-1m: A dataset and benchmark for large-scale face recognition
Guo, Y.; Zhang, L.; Hu, Y.; He, X.; and Gao, J. 2016 · 2016
Later among the works it cites.
A methodology for formalizing model-inversion attacks
Wu, X.; Fredrikson, M.; Jha, S.; and Naughton, J. F. 2016 · 2016
Later among the works it cites.
On calibration of modern neural networks
Guo, C.; Pleiss, G.; Sun, Y.; and Weinberger, K. Q. 2017 · 2017
Later among the works it cites.
Opening the black box of deep neural networks via information
Shwartz-Ziv, R.; and Tishby, N. 2017 · 2017
Later among the works it cites.
Wang, Y.-X. 2018 · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Fredrikson, M.; Jha, S.; and Ristenpart, T. 2015 · 2015
Cited alongside, same era.
Deep learning with differential privacy
Abadi, M.; Chu, A.; Goodfellow, I.; McMahan, H. B.; Mironov, I.; Talwar, K.; and Zhang, L. 2016 · 2016
Cited alongside, same era.
Deep variational information bottleneck
Alemi, A. A.; Fischer, I.; Dillon, J. V.; and Murphy, K. 2016 · 2016
Cited alongside, same era.
Privacy risk in machine learning: Analyzing the connection to overfitting
Yeom, S.; Giacomelli, I.; Fredrikson, M.; and Jha, S. 2018 · 2018
Later among the works it cites.
Nonlinear information bottleneck
Kolchinsky, A.; Tracey, B. D.; and Wolpert, D. H. 2019 · 2019
Later among the works it cites.