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In privacy-preserving data analysis, many procedures and algorithms are structured as compositions of multiple private building blocks.
On the remainder in the central limit theorem: Part i. onedimensional independent variables with finite absolute moments of third order
H. Prawitz · 1975
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Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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Boosting and differential privacy
C. Dwork, G. N. Rothblum, and S. Vadhan · 2010
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Refinement of estimates for the rate of convergence in lyapunov’s theorem
I. Shevtsova · 2010
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Differentially private empirical risk minimization
K. Chaudhuri, C. Monteleoni, and A. D. Sarwate · 2011
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The bootstrap and Edgeworth expansion
P. Hall · 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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The composition theorem for differential privacy
P. Kairouz, S. Oh, and P. Viswanath · 2015
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Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
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Concentrated differential privacy: Simplifications, extensions, and lower bounds
M. Bun and T. Steinke · 2016
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Concentrated differential privacy
C. Dwork and G. N. Rothblum · 2016
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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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Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising
B. Balle and Y.-X. Wang · 2018
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Composable and versatile privacy via truncated cdp
M. Bun, C. Dwork, G. N. Rothblum, and T. Steinke · 2018
Federated analytics: Collaborative data science without data collection
D. Ramage and S. Mazzocchi · 2020
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Sharp composition bounds for gaussian differential privacy via edgeworth expansion
Q. Zheng, J. Dong, Q. Long, and W. J. Su · 2020
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Federated heavy hitters discovery with differential privacy
W. Zhu, P. Kairouz, B. McMahan, H. Sun, and W. Li · 2020
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Explicit non-asymptotic bounds for the distance to the first-order edgeworth expansion
A. Derumigny, L. Girard, and Y. Guyonvarch · 2021
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Numerical composition of differential privacy
S. Gopi, Y. T. Lee, and L. Wutschitz · 2021
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Subsampled rényi differential privacy and analytical moments accountant
Y.-X. Wang, B. Balle, and S. P. Kasiviswanathan · 2019
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Deep learning with Gaussian differential privacy
Z. Bu, J. Dong, Q. Long, and W. J. Su · 2020
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Computing tight differential privacy guarantees using fft
A. Koskela, J. Jälkö, and A. Honkela · 2020
Cited alongside, same era.
Federated analytics: Opportunities and challenges
D. Wang, S. Shi, Y. Zhu, and Z. Han · 2021
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Optimal accounting of differential privacy via characteristic function
Y. Zhu, J. Dong, and Y.-X. Wang · 2021
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Gaussian differential privacy
J. Dong, A. Roth, and W. J. Su · 2022
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