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20172025
most citedDifferentially Private Bayesian Learning on Distributed Data

34 citations · 86 across the 12 of their papers we have counts for

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2 papers · 1 filter

cs.CR2021★ 2 cited

Tight Accounting in the Shuffle Model of Differential Privacy

Antti Koskela, Mikko A. Heikkilä, Antti Honkela

Shuffle model of differential privacy is a novel distributed privacy model based on a combination of local privacy mechanisms and a secure shuffler. It has been shown that the addi…

cs.CR2020★ 22 cited

Differentially private cross-silo federated learning

Mikko A. Heikkilä, Antti Koskela, Kana Shimizu +2

Strict privacy is of paramount importance in distributed machine learning. Federated learning, with the main idea of communicating only what is needed for learning, has been recent…