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We explore and compare a variety of definitions for privacy and disclosure limitation in statistical estimation and data analysis, including (approximate) differential privacy, testing-based definitions of privacy, and posterior guarantees on disclosure risk.
Metric entropy and approximation
G. G. Lorentz · 1966
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Robust Statistics
P. J. Huber · 1981
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Nonparametric Density Estimation: The L 1 L_{1} View
L. Devroye and L. Györfi · 1985
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Convex Analysis and Minimization Algorithms I
J. Hiriart-Urruty and C. Lemaréchal · 1996
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An elementary introduction to modern convex geometry
K. Ball · 1997
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Foundations of Modern Probability
O. Kallenberg · 1997
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B. Yu · 1997
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Theory of Point Estimation, Second Edition
E. L. Lehmann and G. Casella · 1998
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Information-theoretic determination of minimax rates of convergence
Y. Yang and A. Barron · 1999
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Asymptotics in Statistics: Some Basic Concepts
L. Le Cam and G. L. Yang · 2000
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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 divergences and informations in statistics and information theory
F. Liese and I. Vajda · 2006
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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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Differential privacy: a survey of results
C. Dwork · 2008
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Differential privacy for statistics: what we know and what we want to learn
C. Dwork and A. Smith · 2009
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Privacy-preserving statistical estimation with optimal convergence rates
A. Smith · 2011
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Near-optimal algorithms for differentially-private principal components
K. Chaudhuri, A. Sarwate, and K. Sinha · 2012
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A rigorous and customizable framework for privacy
D. Kifer and A. Machanavajjhala · 2012
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Coupled-worlds privacy: Exploiting adversarial uncertainty in statistical data privacy
R. Bassily, A. Groce, J. Katz, and A. Smith · 2013
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Bounds on the sample complexity for private learning and private data release
A. Beimel, H. Brenner, S. P. Kasiviswanathan, and K. Nissim · 2013
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Local privacy and statistical minimax rates
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Introduction to Nonparametric Estimation
A. B. Tsybakov · 2009
Cited alongside, same era.
A multiplicative weights mechanism for privacy-preserving data analysis
M. Hardt and G. N. Rothblum · 2010
Cited alongside, same era.
On the geometry of differential privacy
M. Hardt and K. Talwar · 2010
Cited alongside, same era.
A statistical framework for differential privacy
L. Wasserman and S. Zhou · 2010
Cited alongside, same era.
Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith
Cited in the paper.
J. C. Duchi, M. I. Jordan, and M. J. Wainwright · 2013
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New Statistical Applications for Differential Privacy
R. Hall · 2013
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On the ’semantics’ of differential privacy: A Bayesian formulation
S. P. Kasiviswanathan and A. Smith · 2013
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The geometry of differential privacy: the sparse and approximate case
A. Nikolov, K. Talwar, and L. Zhang · 2013
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The composition theorem for differential privacy
S. Oh and P. Viswanath · 2013
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