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In this paper, we initiate a systematic investigation of differentially private algorithms for convex empirical risk minimization.
Extension of function satisfying a lipschitz condition
J. Czipser and L. Geher · 1955
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Norm-preserving extension of convex lipschitz functions
S. Cobzas and C. Mustata · 1978
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Problem Complexity and Method Efficiency in Optimization
A. S. Nemirovski and D. B. Yudin · 1983
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Sampling and integration of near log-concave functions
David Applegate and Ravi Kannan · 1991
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Revealing information while preserving privacy
Irit Dinur and Kobbi Nissim · 2003
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Simulated annealing in convex bodies and an o ∗ ( n 4 ) o^{*}(n^{4}) volume algorithm
L. Lovasz and S. Vempala · 2003
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Convex Optimization
Stephen Boyd and Lieven Vandenberghe · 2004
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Privacy-preserving datamining on vertically partitioned databases
Cynthia Dwork and Kobbi Nissim · 2004
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Practical privacy: The SuLQ framework
Avrim Blum, Cynthia Dwork, Frank McSherry, and Kobbi Nissim · 2005
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Online convex optimization in the bandit setting: gradient descent without a gradient
Abraham D Flaxman, Adam Tauman Kalai, and H Brendan McMahan · 2005
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Evolving sets, mixing and heat kernel bounds
Ben Morris and Yuval Peres · 2005
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Differential privacy
Cynthia Dwork · 2006
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The geometry of logconcave functions and sampling algorithms
László Lovász and Santosh Vempala · 2007
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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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Privacy-preserving logistic regression
Kamalika Chaudhuri and Claire Monteleoni · 2008
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A note on differential privacy: Defining resistance to arbitrary side information
Shiva Prasad Kasiviswanathan and Adam Smith · 2008
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What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2008
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Fast rates for regularized objectives
Karthik Sridharan, Shai Shalev-shwartz, and Nathan Srebro · 2008
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Learning in a large function space: Privacy-preserving mechanisms for svm learning
Benjamin I. P. Rubinstein, Peter L. Bartlett, Ling Huang, and Nina Taft · 2009
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Stochastic Convex Optimization
Differentially private online learning
Prateek Jain, Pravesh Kothari, and Abhradeep Thakurta · 2012
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A rigorous and customizable framework for privacy
Daniel Kifer and Ashwin Machanavajjhala · 2012
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Private convex empirical risk minimization and high-dimensional regression
Daniel Kifer, Adam Smith, and Abhradeep Thakurta · 2012
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A near-optimal algorithm for differentially-private principal components
Kamalika Chaudhuri, Anand D. Sarwate, and Kaushik Sinha · 2013
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Local privacy and statistical minimax rates
John C. Duchi, Michael I. Jordan, and Martin J. Wainwright · 2013
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Differentially private learning with kernels
Prateek Jain and Abhradeep Thakurta · 2013
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Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, and Karthik Sridharan · 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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On the geometry of differential privacy
Moritz Hardt and Kunal Talwar · 2010
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Probabilistic inference and differential privacy
Oliver Williams and Frank McSherry · 2010
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Sample complexity bounds for differentially private learning
Kamalika Chaudhuri and Daniel Hsu · 2011
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D. Sarwate · 2011
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On differentially private low rank approximation
Michael Kapralov and Kunal Talwar · 2013
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The power of linear reconstruction attacks
Shiva Prasad Kasiviswanathan, Mark Rudelson, and Adam Smith · 2013
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The geometry of differential privacy: The sparse and approximate cases
Aleksandar Nikolov, Kunal Talwar, and Li Zhang · 2013
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Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes
Ohad Shamir and Tong Zhang · 2013
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Stochastic gradient descent with differentially private updates
S. Song, K. Chaudhuri, and A.D. Sarwate · 2013
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Fingerprinting codes and the price of approximate differential privacy
Mark Bun, Jonathan Ullman, and Salil Vadhan · 2014
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(near) dimension independent risk bounds for differentially private learning
Prateek Jain and Abhradeep Thakurta · 2014
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