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Differentially Private (DP) data release is a promising technique to disseminate data without compromising the privacy of data subjects.
Multicollinearity in regression analysis: the problem revisited
Donald E Farrar and Robert R Glauber · 1967
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Extensions of lipschitz mappings into a hilbert space 26
William B Johnson and Joram Lindenstrauss · 1984
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Health insurance portability and accountability act of 1996
Accountability Act · 1996
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Database-friendly random projections: Johnson-lindenstrauss with binary coins
Dimitris Achlioptas · 2003
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Regression analysis by example , volume 607
Samprit Chatterjee and Ali S Hadi · 2006
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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The million song dataset
Thierry Bertin-Mahieux, Daniel P.W. Ellis, Brian Whitman, and Paul Lamere · 2011
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Yahoo! learning to rank challenge overview
Olivier Chapelle and Yi Chang · 2011
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Sample complexity bounds for differentially private learning
Kamalika Chaudhuri and Daniel Hsu · 2011
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A firm foundation for private data analysis
Cynthia Dwork · 2011
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Secure multiple linear regression based on homomorphic encryption
Rob Hall, Stephen E Fienberg, and Yuval Nardi · 2011
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What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
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Convergence rates for differentially private statistical estimation
Kamalika Chaudhuri and Daniel Hsu · 2012
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Privacy via the johnson-lindenstrauss transform
Krishnaram Kenthapadi, Aleksandra Korolova, Ilya Mironov, and Nina Mishra · 2012
Cited alongside, same era.
Privacy-preserving ridge regression on hundreds of millions of records
Valeria Nikolaenko, Udi Weinsberg, Stratis Ioannidis, Marc Joye, Dan Boneh, and Nina Taft · 2013
Cited alongside, same era.
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, Aaron Roth, et al · 2014
Cited alongside, same era.
Event labeling combining ensemble detectors and background knowledge
Hadi Fanaee-T and Joao Gama · 2014
Cited alongside, same era.
Cltune: A generic auto-tuner for opencl kernels
Dppro: Differentially private high-dimensional data release via random projection
Chugui Xu, Ju Ren, Yaoxue Zhang, Zhan Qin, and Kui Ren · 2017
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A data-driven statistical model for predicting the critical temperature of a superconductor
Kam Hamidieh · 2018
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Pate-gan: Generating synthetic data with differential privacy guarantees
James Jordon, Jinsung Yoon, and Mihaela Van Der Schaar · 2018
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California consumer privacy act (CCPA)
California State Legislature · 2018
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Differentially private generative adversarial network
Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, and Jiayu Zhou · 2018
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Cedric Nugteren and Valeriu Codreanu · 2015
Cited alongside, same era.
A few good inequalities, November 2015
David Pollard · 2015
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General data protection regulation (GDPR), 2016
European Parliament and Council of the European Union · 2016
Cited alongside, same era.
UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
Cited alongside, same era.
Long-term trends in the public perception of artificial intelligence
Ethan Fast and Eric Horvitz · 2017
Cited alongside, same era.
Privacy-preserving distributed linear regression on high-dimensional data
Adrià Gascón, Phillipp Schoppmann, Borja Balle, Mariana Raykova, Jack Doerner, Samee Zahur, and David Evans · 2017
Cited alongside, same era.
Youtube-boundingboxes: A large high-precision human-annotated data set for object detection in video
Esteban Real, Jonathon Shlens, Stefano Mazzocchi, Xin Pan, and Vincent Vanhoucke · 2017
Cited alongside, same era.
Sobol tensor trains for global sensitivity analysis
Rafael Ballester-Ripoll, Enrique G Paredes, and Renato Pajarola · 2019
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Private stochastic convex optimization with optimal rates
Raef Bassily, Vitaly Feldman, Kunal Talwar, and Abhradeep Thakurta · 2019
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Machine learning with R: expert techniques for predictive modeling
Brett Lantz · 2019
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Synthesizing differentially private datasets using random mixing
Kangwook Lee, Hoon Kim, Kyungmin Lee, Changho Suh, and Kannan Ramchandran · 2019
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Private stochastic convex optimization: optimal rates in linear time
Vitaly Feldman, Tomer Koren, and Kunal Talwar · 2020
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Differentially private small dataset release using random projections
Lovedeep Gondara and Ke Wang · 2020
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Giantmidi-piano: A large-scale midi dataset for classical piano music
Qiuqiang Kong, Bochen Li, Jitong Chen, and Yuxuan Wang · 2020
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