212 citations · 505 across the 43 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…
Individual Privacy Accounting with Gaussian Differential Privacy
Antti Koskela, Marlon Tobaben, Antti Honkela
Individual privacy accounting enables bounding differential privacy (DP) loss individually for each participant involved in the analysis. This can be informative as often the indiv…
Differentially private partitioned variational inference
Mikko A. Heikkilä, Matthew Ashman, Siddharth Swaroop +2
Learning a privacy-preserving model from sensitive data which are distributed across multiple devices is an increasingly important problem. The problem is often formulated in the f…
Noise-Aware Statistical Inference with Differentially Private Synthetic Data
Ossi Räisä, Joonas Jälkö, Samuel Kaski +1
While generation of synthetic data under differential privacy (DP) has received a lot of attention in the data privacy community, analysis of synthetic data has received much less.…