Fetching the paper…
Reading the bibliography…
Machine-learning models contain information about the data they were trained on.
Efficiency versus protection in a general randomized response model
Harald Anderson · 1977
Earlier work this paper cites.
Iteratively reweighted least squares for maximum likelihood estimation, and some robust and resistant alternatives
Peter J Green · 1984
Earlier work this paper cites.
Fundamentals of statistical signal processing
Steven M Kay · 1993
Earlier work this paper cites.
Iterative reweighted least-squares design of fir filters
C Sidney Burrus, JA Barreto, and Ivan W Selesnick · 1994
Earlier work this paper cites.
The MNIST database of handwritten digits, 1998
Y. LeCun and C. Cortes · 1998
Earlier work this paper cites.
Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression
P. Samarati and L. Sweeney · 1998
Earlier work this paper cites.
On the design and quantification of privacy preserving data mining algorithms
Dakshi Agrawal and Charu C Aggarwal · 2001
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.
Theory of point estimation
Erich L Lehmann and George Casella · 2006
Earlier work this paper cites.
t-closeness: Privacy beyond k-anonymity and l-diversity
Ninghui Li, Tiancheng Li, and Suresh Venkatasubramanian · 2007
Earlier work this paper cites.
l-diversity: Privacy beyond k-anonymity
Ashwin Machanavajjhala, Daniel Kifer, Johannes Gehrke, and Muthuramakrishnan Venkitasubramaniam · 2007
Earlier work this paper cites.
Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
Earlier work this paper cites.
The matrix cookbook, 2007
Kaare Brandt Petersen and Michael Syskind Pedersen · 2007
Earlier work this paper cites.
On thesemantics’ of differential privacy: A bayesian formulation
Shiva Prasad Kasiviswanathan and Adam Smith · 2008
Earlier work this paper cites.
Estimation of the warfarin dose with clinical and pharmacogenetic data
T.E. Klein, R.B. Altman, N. Eriksson, B.F. Gage, S.E. Kimmel, M.-T.M. Lee, N.A. Limdi, D. Page, D.M. Roden, M.J. Wagner, M.D. Caldwell, and J.A. Johnson · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky · 2009
Cited alongside, same era.
Iteratively reweighted least squares minimization for sparse recovery
Ingrid Daubechies, Ronald DeVore, Massimo Fornasier, and C Sinan Güntürk · 2010
Cited alongside, same era.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
Cited alongside, same era.
Theory of statistics
Mark J Schervish · 2012
Cited alongside, same era.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
Cited alongside, same era.
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
Cited alongside, same era.
Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
Later among the works it cites.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Later among the works it cites.
Privacy for all: Ensuring fair and equitable privacy protections
Michael D Ekstrand, Rezvan Joshaghani, and Hoda Mehrpouyan · 2018
Later among the works it cites.
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
Later among the works it cites.
Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 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…
Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
Cited alongside, same era.
Inferential privacy guarantees for differentially private mechanisms
Arpita Ghosh and Robert Kleinberg · 2016
Cited alongside, same era.
Dependence makes you vulnberable: Differential privacy under dependent tuples
Changchang Liu, Supriyo Chakraborty, and Prateek Mittal · 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.
UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
Cited alongside, same era.
Fisher information as a measure of privacy: Preserving privacy of households with smart meters using batteries
Farhad Farokhi and Henrik Sandberg · 2017
Cited alongside, same era.
The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song · 2019
Later among the works it cites.
On the compatibility of privacy and fairness
Rachel Cummings, Varun Gupta, Dhamma Kimpara, and Jamie Morgenstern · 2019
Later among the works it cites.
Jinshuo Dong, Aaron Roth, and Weijie J Su · 2019
Later among the works it cites.
Improved convergence for ℓ 1 \ell_{1} and ℓ ∞ \ell_{\infty} regression via iteratively reweighted least squares
Alina Ene and Adrian Vladu · 2019
Later among the works it cites.
Disparate vulnerability: On the unfairness of privacy attacks against machine learning
Mohammad Yaghini, Bogdan Kulynych, and Carmela Troncoso · 2019
Later among the works it cites.
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 · 2020
Later among the works it cites.
Modelling and quantifying membership information leakage in machine learning
Farhad Farokhi and Mohamed Ali Kaafar · 2020
Later among the works it cites.
Differentially private learning does not bound membership inference
Thomas Humphries, Matthew Rafuse, Lindsey Tulloch, Simon Oya, Ian Goldberg, and Florian Kerschbaum · 2020
Later among the works it cites.
Black-box model inversion attribute inference attacks on classification models
Shagufta Mehnaz, Ninghui Li, and Elisa Bertino · 2020
Later among the works it cites.