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A fundamental result in differential privacy states that the privacy guarantees of a mechanism are preserved by any post-processing of its output.
The privacy blanket of the shuffle model
B. Balle, J. Bell, A. Gascón, and K. Nissim · 1903
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Sur les proprietes asymptotiques de mouvements rÉgis par certains types de chaÎnes simples (suite et fin)
W. Doeblin · 1937
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Central limit theorem for nonstationary Markov chains. I
R. L. Dobrushin · 1956
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Relative entropy under mappings by stochastic matrices
J. E. Cohen, Y. Iwasa, G. Rautu, M. B. Ruskai, E. Seneta, and G. Zbaganu · 1993
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Lectures on the coupling method
T. Lindvall · 2002
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On contraction properties of Markov kernels
P. Del Moral, M. Ledoux, and L. Miclo · 2003
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Stochastic differential equations
B. Øksendal · 2003
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General irreducible Markov chains and non-negative operators , volume 83
E. Nummelin · 2004
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When random sampling preserves privacy
K. Chaudhuri and N. Mishra · 2006
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Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
Cited alongside, same era.
What can we learn privately?
S. P. Kasiviswanathan, H. K. Lee, K. Nissim, S. Raskhodnikova, and A. Smith · 2011
Cited alongside, same era.
On sampling, anonymization, and differential privacy or, k-anonymization meets differential privacy
N. Li, W. Qardaji, and D. Su · 2012
Cited alongside, same era.
Markov chains and stochastic stability
S. P. Meyn and R. L. Tweedie · 2012
Cited alongside, same era.
Analysis and geometry of Markov diffusion operators , volume 348
D. Bakry, I. Gentil, and M. Ledoux · 2013
Cited alongside, same era.
Beyond differential privacy: Composition theorems and relational logic for f-divergences between probabilistic programs
G. Barthe and F. Olmedo · 2013
Differentially private release and learning of threshold functions
M. Bun, K. Nissim, U. Stemmer, and S. Vadhan · 2015
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Strong data processing inequalities and Φ \Phi -Sobolev inequalities for discrete channels
M. Raginsky · 2016
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Markov chains and mixing times , volume 107
D. A. Levin and Y. Peres · 2017
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Rényi differential privacy
I. Mironov · 2017
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Privacy amplification by subsampling: Tight analyses via couplings and divergences
B. Balle, G. Barthe, and M. Gaboardi · 2018
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Privacy amplification by iteration
V. Feldman, I. Mironov, K. Talwar, and A. Thakurta · 2018
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Cited alongside, same era.
Characterizing the sample complexity of private learners
A. Beimel, K. Nissim, and U. Stemmer · 2013
Cited alongside, same era.
Bounds on the sample complexity for private learning and private data release
A. Beimel, H. Brenner, S. P. Kasiviswanathan, and K. Nissim · 2014
Cited alongside, same era.
Convex optimization: Algorithms and complexity
S. Bubeck · 2015
Cited alongside, same era.
Distributed differential privacy via shuffling
A. Cheu, A. D. Smith, J. Ullman, D. Zeber, and M. Zhilyaev · 2019
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Amplification by shuffling: From local to central differential privacy via anonymity
Ú. Erlingsson, V. Feldman, I. Mironov, A. Raghunathan, K. Talwar, and A. Thakurta · 2019
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Subsampled rényi differential privacy and analytical moments accountant
Y.-X. Wang, B. Balle, and S. Kasiviswanathan · 2019
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