Fetching the paper…
Reading the bibliography…
We study membership inference in settings where some of the assumptions typically used in previous research are relaxed.
RCV1: A new benchmark collection for text categorization research
David D Lewis, Yiming Yang, Tony G Rose, and Fan Li · 2004
Earlier work this paper cites.
Adversarial learning
Daniel Lowd and Christopher Meek · 2005
Earlier work this paper cites.
Calibrating Noise to Sensitivity in Private Data Analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 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
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Genomic privacy and limits of individual detection in a pool
Sriram Sankararaman, Guillaume Obozinski, Michael I Jordan, and Eran Halperin · 2009
Earlier work this paper cites.
A statistical framework for differential privacy
Larry Wasserman and Shuheng Zhou · 2010
Earlier work this paper cites.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D. Sarwate · 2011
Earlier work this paper cites.
Acquire valued shoppers challenge, 2014
Kaggle Competition · 2014
Earlier work this paper cites.
Privacy in Pharmacogenetics: An end-to-end case study of personalized Warfarin dosing
Matthew Fredrikson, Eric Lantz, Somesh Jha, Simon Lin, David Page, and Thomas Ristenpart · 2014
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers
Giuseppe Ateniese, Luigi Mancini, Angelo Spognardi, Antonio Villani, Domenico Vitali, and Giovanni Felici · 2015
Earlier work this paper cites.
Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
Earlier work this paper cites.
Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 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
Cited alongside, same era.
Stealing machine learning models via prediction APIs
Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart · 2016
Cited alongside, same era.
The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2017
Cited alongside, same era.
Towards measuring membership privacy
Yunhui Long, Vincent Bindschaedler, and Carl A. Gunter · 2017
Cited alongside, same era.
Rényi differential privacy
Ilya Mironov · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
TensorFlow Privacy
Galen Andrew, Steve Chien, and Nicolas Papernot · 2019
Later among the works it cites.
Hypothesis testing interpretations and renyi differential privacy
Borja Balle, Gilles Barthe, Marco Gaboardi, Justin Hsu, and Tetsuya Sato · 2019
Later among the works it cites.
Deep learning with gaussian differential privacy
Zhiqi Bu, Jinshuo Dong, Qi Long, and Weijie J Su · 2019
Later among the works it cites.
Jinshuo Dong, Aaron Roth, and Weijie J Su · 2019
Later among the works it cites.
Evaluating differentially private machine learning in practice
Bargav Jayaraman and David Evans · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Code for membership inference attack against machine learning models
Congzheng Song · 2017
Cited alongside, same era.
Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising
Borja Balle and Yu-Xiang Wang · 2018
Cited alongside, same era.
Property inference attacks on fully connected neural networks using permutation invariant representations
Karan Ganju, Qi Wang, Wei Yang, Carl A Gunter, and Nikita Borisov · 2018
Cited alongside, same era.
Distributed learning without distress: Privacy-preserving empirical risk minimization
Bargav Jayaraman, Lingxiao Wang, David Evans, and Quanquan Gu · 2018
Cited alongside, same era.
Understanding membership inferences on well-generalized learning models
Yunhui Long, Vincent Bindschaedler, Lei Wang, Diyue Bu, Xiaofeng Wang, Haixu Tang, Carl A Gunter, and Kai Chen · 2018
Cited alongside, same era.
Membership inference attack against differentially private deep learning model
Md Atiqur Rahman, Tanzila Rahman, Robert Laganiere, Noman Mohammed, and Yang Wang · 2018
Cited alongside, same era.
Memguard: Defending against black-box membership inference attacks via adversarial examples
Jinyuan Jia, Ahmed Salem, Michael Backes, Yang Zhang, and Neil Zhenqiang Gong · 2019
Later among the works it cites.
Investigating statistical privacy frameworks from the perspective of hypothesis testing
Changchang Liu, Xi He, Thee Chanyaswad, Shiqiang Wang, and Prateek Mittal · 2019
Later among the works it cites.
Comprehensive privacy analysis of deep learning
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2019
Later among the works it cites.
ML-Leaks: Model and data independent membership inference attacks and defenses on machine learning models
Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, and Michael Backes · 2019
Later among the works it cites.
Label-only membership inference attacks
Christopher A Choquette Choo, Florian Tramer, Nicholas Carlini, and Nicolas Papernot · 2020
Closest in time.
Modelling and quantifying membership information leakage in machine learning
Farhad Farokhi and Mohamed Ali Kaafar · 2020
Closest in time.
Systematic evaluation of privacy risks of machine learning models
Liwei Song and Prateek Mittal · 2020
Closest in time.
Cache telepathy: Leveraging shared resource attacks to learn DNN architectures
Mengjia Yan, Christopher Fletcher, and Josep Torrellas · 2020
Closest in time.