212 citations · 505 across the 43 of their papers we have counts for
5 papers · 1 filter
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…
Differentially Private Federated Variational Inference
Mrinank Sharma, Michael Hutchinson, Siddharth Swaroop +2
In many real-world applications of machine learning, data are distributed across many clients and cannot leave the devices they are stored on. Furthermore, each client's data, comp…
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…
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…