20 citations · 21 across the 9 of their papers we have counts for
3 papers · 1 filter
How Do Input Attributes Impact the Privacy Loss in Differential Privacy?
Tamara T. Mueller, Stefan Kolek, Friederike Jungmann +5
Differential privacy (DP) is typically formulated as a worst-case privacy guarantee over all individuals in a database. More recently, extensions to individual subjects or their at…
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…
Partial sensitivity analysis in differential privacy
Tamara T. Mueller, Alexander Ziller, Dmitrii Usynin +4
Differential privacy (DP) allows the quantification of privacy loss when the data of individuals is subjected to algorithmic processing such as machine learning, as well as the pro…