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Composition is one of the most important properties of differential privacy (DP), as it allows algorithm designers to build complex private algorithms from DP primitives.
The role of interactivity in local differential privacy
M. Joseph, J. Mao, S. Neel, and A. Roth · 1904
Earlier work this paper cites.
J. Dong, A. Roth, and W. J. Su · 1905
Earlier work this paper cites.
Practical differentially private top-k selection with pay-what-you-get composition
D. Durfee and R. Rogers · 1905
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Privacy odometers and filters: Pay-as-you-go composition
R. M. Rogers, A. Roth, J. Ullman, and S. P. Vadhan · 1929
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Comparison of experiments
D. Blackwell · 1950
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Randomized response: a survey technique for eliminating evasive answer bias
S. Warner · 1965
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Our data, ourselves: Privacy via distributed noise generation
C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor · 2006
Cited alongside, same era.
Mechanism design via differential privacy
F. McSherry and K. Talwar · 2007
Cited alongside, same era.
Boosting and differential privacy
C. Dwork, G. N. Rothblum, and S. P. Vadhan · 2010
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.
The composition theorem for differential privacy
S. Oh and P. Viswanath · 2013
Cited alongside, same era.
Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
Concentrated differential privacy: Simplifications, extensions, and lower bounds
M. Bun and T. Steinke · 2016
Later among the works it cites.
The complexity of computing the optimal composition of differential privacy
J. Murtagh and S. Vadhan · 2016
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The composition theorem for differential privacy
P. Kairouz, S. Oh, and P. Viswanath · 2017
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Is interaction necessary for distributed private learning?
A. Smith, A. Thakurta, and J. Upadhyay · 2017
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Lower bounds for locally private estimation via communication complexity
J. Duchi and R. Rogers · 2019
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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.
Privacy odometers and filters: Pay-as-you-go composition
R. M. Rogers, A. Roth, J. Ullman, and S. P. Vadhan
Cited in the paper.