6 citations · 12 across the 24 of their papers we have counts for
3 papers · 1 filter
Beyond the Calibration Point: Mechanism Comparison in Differential Privacy
Georgios Kaissis, Stefan Kolek, Borja Balle +2
In differentially private (DP) machine learning, the privacy guarantees of DP mechanisms are often reported and compared on the basis of a single -pair. This prac…
Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging
Alexander Ziller, Tamara T. Mueller, Simon Stieger +5
Artificial Intelligence (AI) models are vulnerable to information leakage of their training data, which can be highly sensitive, for example in medical imaging. Privacy Enhancing T…
A unified interpretation of the Gaussian mechanism for differential privacy through the sensitivity index
Georgios Kaissis, Moritz Knolle, Friederike Jungmann +3
The Gaussian mechanism (GM) represents a universally employed tool for achieving differential privacy (DP), and a large body of work has been devoted to its analysis. We argue that…