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stat.ML2022
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…
stat.ML2020★ 3 cited
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…