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A wide variety of fundamental data analyses in machine learning, such as linear and logistic regression, require minimizing a convex function defined by the data.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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A learning theory approach to non-interactive database privacy
Avrim Blum, Katrina Ligett, and Aaron Roth · 2008
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Differential privacy and robust statistics
Cynthia Dwork and Jing Lei · 2009
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On the complexity of differentially private data release: efficient algorithms and hardness results
Cynthia Dwork, Moni Naor, Omer Reingold, Guy N. Rothblum, and Salil P. Vadhan · 2009
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Boosting and differential privacy
Cynthia Dwork, Guy N. Rothblum, and Salil P. Vadhan · 2010
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A multiplicative weights mechanism for privacy-preserving data analysis
Moritz Hardt and Guy N. Rothblum · 2010
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Interactive privacy via the median mechanism
Aaron Roth and Tim Roughgarden · 2010
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D. Sarwate · 2011
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Privately releasing conjunctions and the statistical query barrier
Anupam Gupta, Moritz Hardt, Aaron Roth, and Jonathan Ullman · 2011
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PCPs and the hardness of generating private synthetic data
Jonathan Ullman and Salil P. Vadhan · 2011
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The multiplicative weights update method: a meta-algorithm and applications
Sanjeev Arora, Elad Hazan, and Satyen Kale · 2012
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Iterative constructions and private data release
Anupam Gupta, Aaron Roth, and Jonathan Ullman · 2012
Cited alongside, same era.
A simple and practical algorithm for differentially private data release
Moritz Hardt, Katrina Ligett, and Frank McSherry · 2012
Cited alongside, same era.
Private data release via learning thresholds
Moritz Hardt, Guy N. Rothblum, and Rocco A. Servedio · 2012
Cited alongside, same era.
Private convex optimization for empirical risk minimization with applications to high-dimensional regression
Differentially private feature selection via stability arguments, and the robustness of the lasso
Abhradeep Thakurta and Adam Smith · 2013
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Answering n 2+o(1)
Jonathan Ullman · 2013
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Private empirical risk minimization, revisited
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
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Fingerprinting codes and the price of approximate differential privacy
Mark Bun, Jonathan Ullman, and Salil P. Vadhan · 2014
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Faster private release of marginals on small databases
Karthekeyan Chandrasekaran, Justin Thaler, Jonathan Ullman, and Andrew Wan · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Daniel Kifer, Adam D. Smith, and Abhradeep Thakurta · 2012
Cited alongside, same era.
Faster algorithms for privately releasing marginals
Justin Thaler, Jonathan Ullman, and Salil P. Vadhan · 2012
Cited alongside, same era.
Private learning and sanitization: Pure vs. approximate differential privacy
Amos Beimel, Kobbi Nissim, and Uri Stemmer · 2013
Cited alongside, same era.
Efficient algorithms for privately releasing marginals via convex relaxations
Cynthia Dwork, Aleksandar Nikolov, and Kunal Talwar · 2013
Cited alongside, same era.
The power of linear reconstruction attacks
Shiva Prasad Kasiviswanathan, Mark Rudelson, and Adam Smith · 2013
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Preventing false discovery in interactive data analysis is hard
Moritz Hardt and Jonathan Ullman · 2014
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(near) dimension independent risk bounds for differentially private learning
Prateek Jain and Abhradeep Guha Thakurta · 2014
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More general queries with better generalization error in adaptive data analysis
Raef Bassily, Adam Smith, Thomas Steinke, and Jonathan Ullman · 2015
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Preserving statistical validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Roth · 2015
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