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Ensuring differential privacy of models learned from sensitive user data is an important goal that has been studied extensively in recent years.
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M. Kearns, R. Schapire and L. Sellie · 1994
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M. Kearns and U. Vazirani · 1994
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“Stability and generalization”
Olivier Bousquet and Andr“’e Elisseeff · 2002
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“Calibrating noise to sensitivity in private data analysis”
C. Dwork, F. McSherry, K. Nissim and A. Smith · 2006
Earlier work this paper cites.
“Mechanism Design via Differential Privacy”
Frank McSherry and Kunal Talwar · 2007
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.
“Differential Privacy and Robust Statistics”
Cynthia Dwork and Jing Lei · 2009
Earlier work this paper cites.
“Differential Privacy for Statistics: What we Know and What we Want to Learn”
Cynthia Dwork and Adam Smith · 2009
Earlier work this paper cites.
“Bounds on the Sample Complexity for Private Learning and Private Data Release”
Amos Beimel, Shiva Kasiviswanathan and Kobbi Nissim · 2010
Earlier work this paper cites.
“Multiparty Differential Privacy via Aggregation of Locally Trained Classifiers”
Manas. Pathak, Shantanu Rane and Bhiksha Raj · 2010
Earlier work this paper cites.
“Learnability, stability and uniform convergence”
Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro and Karthik Sridharan · 2010
Earlier work this paper cites.
“Sample Complexity Bounds for Differentially Private Learning”
Kamalika Chaudhuri and Daniel Hsu · 2011
Earlier work this paper cites.
“Differentially Private Empirical Risk Minimization”
Kamalika Chaudhuri, Claire Monteleoni and Anand. Sarwate · 2011
Cited alongside, same era.
“What Can We Learn Privately?”
Shiva Kasiviswanathan, Homin. Lee, Kobbi Nissim, Sofya Raskhodnikova and Adam Smith · 2011
Cited alongside, same era.
“Private Convex Optimization for Empirical Risk Minimization with Applications to High-dimensional Regression”
Daniel Kifer, Adam. Smith and Abhradeep Thakurta · 2012
Cited alongside, same era.
“Characterizing the sample complexity of private learners”
Amos Beimel, Kobbi Nissim and Uri Stemmer · 2013
Cited alongside, same era.
“Signal Processing and Machine Learning with Differential Privacy: Algorithms and Challenges for Continuous Data”
Anand. Sarwate and Kamalika Chaudhuri · 2013
Cited alongside, same era.
“Differentially Private Feature Selection via Stability Arguments, and the Robustness of the Lasso”
“Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds”
Mark Bun and Thomas Steinke · 2016
Later among the works it cites.
“Learning privately from multiparty data”
Jihun Hamm, Yingjun Cao and Mikhail Belkin · 2016
Later among the works it cites.
“Train faster, generalize better: Stability of stochastic gradient descent”
Moritz Hardt, Ben Recht and Yoram Singer · 2016
Later among the works it cites.
“Generalization for Adaptively-chosen Estimators via Stable Median”
Vitaly Feldman and Thomas Steinke · 2017
Later among the works it cites.
“Rényi Differential Privacy”
Ilya Mironov · 2017
Later among the works it cites.
“Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data”
Nicolas Papernot, Mart“’n Abadi, “’Ulfar Erlingsson, Ian. Goodfellow and Kunal Talwar · 2017
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Adam Smith and Abhradeep Thakurta · 2013
Cited alongside, same era.
“Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds”
R. Bassily, A. Smith and A. Thakurta · 2014
Cited alongside, same era.
“Preserving Statistical Validity in Adaptive Data Analysis” Extended abstract in STOC 2015
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold and Aaron Roth · 2014
Cited alongside, same era.
“The Algorithmic Foundations of Differential Privacy”
Cynthia Dwork and Aaron Roth · 2014
Cited alongside, same era.
“Understanding Machine Learning: From Theory to Algorithms”
Shai Shalev-Shwartz and Shai Ben-David · 2014
Cited alongside, same era.
“Differentially Private Release and Learning of Threshold Functions”
Mark Bun, Kobbi Nissim, Uri Stemmer and Salil. Vadhan · 2015
Cited alongside, same era.
“Sample Complexity Bounds on Differentially Private Learning via Communication Complexity”
Vitaly Feldman and David Xiao · 2015
Cited alongside, same era.
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.
“Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics”
Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha and Jeffrey. Naughton · 2017
Later among the works it cites.
“Differentially Private Empirical Risk Minimization Revisited: Faster and More General”
Di Wang, Minwei Ye and Jinhui Xu · 2017
Later among the works it cites.
“Model-Agnostic Private Learning via Stability”
Raef Bassily, Om Thakkar and Abhradeep Thakurta · 2018
Closest in time.
“The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting Secrets”
Nicholas Carlini, Chang Liu, Jernej Kos, “’Ulfar Erlingsson and Dawn Song · 2018
Closest in time.
“Understanding Membership Inferences on Well-Generalized Learning Models”
Yunhui Long, Vincent Bindschaedler, Lei Wang, Diyue Bu, Xiaofeng Wang, Haixu Tang, Carl. Gunter and Kai Chen · 2018
Closest in time.
“Scalable Private Learning with PATE”
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar and Ulfar Erlingsson · 2018
Closest in time.