34 citations · 78 across the 5 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.ML2017★ 34 cited
Differentially Private Bayesian Learning on Distributed Data
Mikko Heikkilä, Eemil Lagerspetz, Samuel Kaski +3
Many applications of machine learning, for example in health care, would benefit from methods that can guarantee privacy of data subjects. Differential privacy (DP) has become esta…