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Working under a model of privacy in which data remains private even from the statistician, we study the tradeoff between privacy guarantees and the utility of the resulting statistical estimators.
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Disclosure limitation using perturbation and related methods for categorical data
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V. Buldygin and Y. Kozachenko · 2000
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R. R. Phelps · 2001
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Strong converse for identification via quantum channels
R. Ahlswede and A. Winter · 2002
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Limiting privacy breaches in privacy preserving data mining
A. V. Evfimievski, J. Gehrke, and R. Srikant · 2003
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Measurement Error in Nonlinear Models: A Modern Perspective
R. Carroll, D. Ruppert, L. Stefanski, and C. Crainiceanu · 2006
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Elements of Information Theory, Second Edition
T. M. Cover and J. A. Thomas · 2006
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Our data, ourselves: Privacy via distributed noise generation
C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor · 2006
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On the geometry of differential privacy
M. Hardt and K. Talwar · 2010
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Variable selection in measurement error models
Y. Ma and R. Li · 2010
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The limits of two-party differential privacy
A. McGregor, I. Mironov, T. Pitassi, O. Reingold, K. Talwar, and S. Vadhan · 2010
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A statistical framework for differential privacy
L. Wasserman and S. Zhou · 2010
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K. Chaudhuri, C. Monteleoni, and A. D. Sarwate · 2011
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R. Hall, A. Rinaldo, and L. Wasserman · 2011
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Privacy, accuracy, and consistency too: A holistic solution to contingency table release
B. Barak, K. Chaudhuri, C. Dwork, S. Kale, F. McSherry, and K. Talwar · 2007
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Distributed private data analysis: Simultaneously solving how and what
A. Beimel, K. Nissim, and E. Omri · 2008
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A learning theory approach to non-interactive database privacy
A. Blum, K. Ligett, and A. Roth · 2008
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Composition attacks and auxiliary information in data privacy
S. R. Ganta, S. Kasiviswanathan, and A. Smith · 2008
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Differential privacy and robust statistics
C. Dwork and J. Lei · 2009
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Variable selection for partially linear models with measurement errors
H. Ling and R. Li · 2009
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What can we learn privately?
S. P. Kasiviswanathan, H. K. Lee, K. Nissim, S. Raskhodnikova, and A. Smith · 2011
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Privacy-preserving statistical estimation with optimal convergence rates
A. Smith · 2011
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Convergence rates for differentially private statistical estimation
K. Chaudhuri and D. Hsu · 2012
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Lower bounds in differential privacy
A. De · 2012
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High-dimensional regression with noisy and missing data: provable guarantees with nonconvexity
P.-L. Loh and M. J. Wainwright · 2012
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Matrix concentration inequalities via the method of exchangeable pairs
L. W. Mackey, M. I. Jordan, R. Y. Chen, B. Farrell, and J. A. Tropp · 2012
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A unified framework for high-dimensional analysis of M {M} -estimators with decomposable regularizers
S. Negahban, P. Ravikumar, M. Wainwright, and B. Yu · 2012
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Learning in a large function space: privacy-preserving mechanisms for SVM learning
B. I. P. Rubinstein, P. L. Bartlett, L. Huang, and N. Taft · 2012
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V. Anantharam, A. Gohari, S. Kamath, and C. Nair · 2013
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