189 citations · 324 across the 47 of their papers we have counts for
7 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…
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy
Georgios Kaissis, Jamie Hayes, Alexander Ziller +1
We explore Reconstruction Robustness (ReRo), which was recently proposed as an upper bound on the success of data reconstruction attacks against machine learning models. Previous r…
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…
Generalised Likelihood Ratio Testing Adversaries through the Differential Privacy Lens
Georgios Kaissis, Alexander Ziller, Stefan Kolek Martinez de Azagra +1
Differential Privacy (DP) provides tight upper bounds on the capabilities of optimal adversaries, but such adversaries are rarely encountered in practice. Under the hypothesis test…
Privacy: An axiomatic approach
Alexander Ziller, Tamara Mueller, Rickmer Braren +2
The increasing prevalence of large-scale data collection in modern society represents a potential threat to individual privacy. Addressing this threat, for example through privacy-…