22 citations · 33 across the 2 of their papers we have counts for
3 papers
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…
Representation Transfer for Differentially Private Drug Sensitivity Prediction
Teppo Niinimäki, Mikko Heikkilä, Antti Honkela +1
Motivation: Human genomic datasets often contain sensitive information that limits use and sharing of the data. In particular, simple anonymisation strategies fail to provide suffi…
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…