Bounding, Concentrating, and Truncating: Unifying Privacy Loss Composition for Data Analytics

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Boosting and differential privacy

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Deep learning with differential privacy

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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

Original

C. Dwork and G. Rothblum · 2016

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The complexity of computing the optimal composition of differential privacy

J. Murtagh and S. Vadhan · 2016

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Privacy odometers and filters: Pay-as-you-go composition

The composition theorem for differential privacy

P. Kairouz, S. Oh, and P. Viswanath · 2017

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Rényi differential privacy

I. Mironov · 2017

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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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Truncated laplacian mechanism for approximate differential privacy

Original

Q. Geng, W. Ding, R. Guo, and S. Kumar · 2018

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Exponential line-crossing inequalities, 2018

S. R. Howard, A. Ramdas, J. McAuliffe, and J. Sekhon · 2018

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Linkedin’s audience engagements api: A privacy preserving data analytics system at scale, 2020

R. Rogers, S. Subramaniam, S. Peng, D. Durfee, S. Lee, S. K. Kancha, S. Sahay, and P. Ahammad · 2020