11 citations · 12 across the 3 of their papers we have counts for
4 papers · 1 filter
DPVIm: Differentially Private Variational Inference Improved
Joonas Jälkö, Lukas Prediger, Antti Honkela +1
Differentially private (DP) release of multidimensional statistics typically considers an aggregate sensitivity, e.g. the vector norm of a high-dimensional vector. However, differe…
Locally Differentially Private Bayesian Inference
Tejas Kulkarni, Joonas Jälkö, Samuel Kaski +1
In recent years, local differential privacy (LDP) has emerged as a technique of choice for privacy-preserving data collection in several scenarios when the aggregator is not trustw…
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…
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…