3 citations · 4 across the 4 of their papers we have counts for
4 papers
Collaborative Learning From Distributed Data With Differentially Private Synthetic Twin Data
Lukas Prediger, Joonas Jälkö, Antti Honkela +1
Consider a setting where multiple parties holding sensitive data aim to collaboratively learn population level statistics, but pooling the sensitive data sets is not possible. We p…
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…
D3p -- A Python Package for Differentially-Private Probabilistic Programming
Lukas Prediger, Niki Loppi, Samuel Kaski +1
We present d3p, a software package designed to help fielding runtime efficient widely-applicable Bayesian inference under differential privacy guarantees. d3p achieves general appl…
Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT
Antti Koskela, Joonas Jälkö, Lukas Prediger +1
We propose a numerical accountant for evaluating the tight -privacy loss for algorithms with discrete one dimensional output. The method is based on the privacy lo…