Fetching the paper…
Reading the bibliography…
Some machine learning applications involve training data that is sensitive, such as the medical histories of patients in a clinical trial.
Randomized response: A survey technique for eliminating evasive answer bias
Stanley L Warner · 1965
Earlier work this paper cites.
Queries and concept learning
Dana Angluin · 1988
Earlier work this paper cites.
Neural net algorithms that learn in polynomial time from examples and queries
Eric B Baum · 1991
Earlier work this paper cites.
Signature verification using a “Siamese” time delay neural network
Jane Bromley, James W Bentz, Léon Bottou, Isabelle Guyon, Yann LeCun, Cliff Moore, Eduard Säckinger, and Roopak Shah · 1993
Earlier work this paper cites.
Bagging predictors
Leo Breiman · 1994
Earlier work this paper cites.
Weaving technology and policy together to maintain confidentiality
Latanya Sweeney · 1997
Earlier work this paper cites.
Ensemble methods in machine learning
Thomas G Dietterich · 2000
Earlier work this paper cites.
Machine learning for medical diagnosis: history, state of the art and perspective
Igor Kononenko · 2001
Earlier work this paper cites.
k-anonymity: A model for protecting privacy
L. Sweeney · 2002
Earlier work this paper cites.
On k-anonymity and the curse of dimensionality
Charu C Aggarwal · 2005
Earlier work this paper cites.
Model compression
Cristian Bucilua, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
Earlier work this paper cites.
Our data, ourselves: privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
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.
Privacy-preserving logistic regression
Kamalika Chaudhuri and Claire Monteleoni · 2009
Cited alongside, same era.
Boosting and differential privacy
Cynthia Dwork, Guy N Rothblum, and Salil Vadhan · 2010
Cited alongside, same era.
Multiparty differential privacy via aggregation of locally trained classifiers
Manas Pathak, Shantanu Rane, and Bhiksha Raj · 2010
Cited alongside, same era.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
Cited alongside, same era.
A firm foundation for private data analysis
Cynthia Dwork · 2011
Cited alongside, same era.
Privacy preserving probabilistic inference with hidden markov models
Manas Pathak, Shantanu Rane, Wei Sun, and Bhiksha Raj · 2011
Cited alongside, same era.
Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning
Babak Alipanahi, Andrew Delong, Matthew T Weirauch, and Brendan J Frey · 2015
Later among the works it cites.
Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
Later among the works it cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Later among the works it cites.
Siamese neural networks for one-shot image recognition
Gregory Koch · 2015
Later among the works it cites.
Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
Later among the works it cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Privacy aware learning
Martin J Wainwright, Michael I Jordan, and John C Duchi · 2012
Cited alongside, same era.
A semi-supervised learning approach to differential privacy
Geetha Jagannathan, Claire Monteleoni, and Krishnan Pillaipakkamnatt · 2013
Cited alongside, same era.
Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate · 2013
Cited alongside, same era.
Differentially private empirical risk minimization: efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
Cited alongside, same era.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
Cited alongside, same era.
RAPPOR: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
Cited alongside, same era.
Closest in time.
Concentrated differential privacy: simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
Closest in time.
Concentrated differential privacy
Cynthia Dwork and Guy N Rothblum · 2016
Closest in time.
Learning privately from multiparty data
Jihun Hamm, Paul Cao, and Mikhail Belkin · 2016
Closest in time.
Smart reply: Automated response suggestion for email
Anjuli Kannan, Karol Kurach, Sujith Ravi, Tobias Kaufmann, Andrew Tomkins, Balint Miklos, Greg Corrado, et al · 2016
Closest in time.
Renyi differential privacy
Ilya Mironov · 2016
Closest in time.
Missing data imputation for supervised learning
Jason Poulos and Rafael Valle · 2016
Closest in time.
Improved techniques for training GANs
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Closest in time.