Tight Accounting in the Shuffle Model of Differential Privacy

Calibrating noise to sensitivity in private data analysis

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What can we learn privately?

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The algorithmic foundations of differential privacy

Dwork, C. and Roth, A. (2014) · 2014

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Nuyens, D., Suryanarayana, G., and Weimar, M. (2016) · 2016

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Prochlo: Strong privacy for analytics in the crowd

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Rényi differential privacy

Mironov, I. (2017) · 2017

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The privacy blanket of the shuffle model

Balle, B., Bell, J., Gascón, A., and Nissim, K. (2019) · 2019

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Distributed differential privacy via shuffling

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Amplification by shuffling: From local to central differential privacy via anonymity

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Privacy loss classes: The central limit theorem in differential privacy

Sommer, D. M., Meiser, S., and Mohammadi, E. (2019) · 2019

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Private summation in the multi-message shuffle model

Balle, B., Bell, J., Gascón, A., and Nissim, K. (2020) · 2020

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Computing tight differential privacy guarantees using FFT

Koskela, A., Jälkö, J., and Honkela, A. (2020) · 2020

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Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling

On the power of multiple anonymous messages: Frequency estimation and selection in the shuffle model of differential privacy

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Shuffled model of differential privacy in federated learning

Girgis, A., Data, D., Diggavi, S., Kairouz, P., and Suresh, A. T. (2021) · 2021

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Numerical composition of differential privacy

Gopi, S., Lee, Y. T., and Wutschitz, L. (2021) · 2021

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Computing differential privacy guarantees for heterogeneous compositions using fft

Original

Koskela, A. and Honkela, A. (2021) · 2021

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Tight differential privacy for discrete-valued mechanisms and for the subsampled gaussian mechanism using FFT

Koskela, A., Jälkö, J., Prediger, L., and Honkela, A. (2021) · 2021

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