12 citations · 41 across the 9 of their papers we have counts for
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stat.ML2019★ 12 cited
Privacy-preserving data sharing via probabilistic modelling
Joonas Jälkö, Eemil Lagerspetz, Jari Haukka +3
Differential privacy allows quantifying privacy loss resulting from accessing sensitive personal data. Repeated accesses to underlying data incur increasing loss. Releasing data as…
stat.ML2019★ 11 cited
Computing Tight Differential Privacy Guarantees Using FFT
Antti Koskela, Joonas Jälkö, Antti Honkela
Differentially private (DP) machine learning has recently become popular. The privacy loss of DP algorithms is commonly reported using -DP. In this paper, we propo…
stat.ML2019★ 11 cited
Differentially Private Markov Chain Monte Carlo
Mikko A. Heikkilä, Joonas Jälkö, Onur Dikmen +1
Recent developments in differentially private (DP) machine learning and DP Bayesian learning have enabled learning under strong privacy guarantees for the training data subjects. I…