4 papers
Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy
Bogdan Kulynych, Juan Felipe Gomez, Georgios Kaissis +4
Differentially private (DP) mechanisms are difficult to interpret and calibrate because existing methods for mapping standard privacy parameters to concrete privacy risks -- re-ide…
Should I use Synthetic Data for That? An Analysis of the Suitability of Synthetic Data for Data Sharing and Augmentation
Bogdan Kulynych, Theresa Stadler, Jean Louis Raisaro +1
Recent advances in generative modelling have led many to see synthetic data as the go-to solution for a range of problems around data access, scarcity, and under-representation. In…
A Consensus Privacy Metrics Framework for Synthetic Data
Lisa Pilgram, Fida K. Dankar, Jorg Drechsler +12
Synthetic data generation is one approach for sharing individual-level data. However, to meet legislative requirements, it is necessary to demonstrate that the individuals' privacy…
Participatory Assessment of Large Language Model Applications in an Academic Medical Center
Giorgia Carra, Bogdan Kulynych, François Bastardot +3
Although Large Language Models (LLMs) have shown promising performance in healthcare-related applications, their deployment in the medical domain poses unique challenges of ethical…