9 citations · 20 across the 4 of their papers we have counts for
4 papers
Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data
Georgi Ganev, Bristena Oprisanu, Emiliano De Cristofaro
Generative models trained with Differential Privacy (DP) can be used to generate synthetic data while minimizing privacy risks. We analyze the impact of DP on these models vis-a-vi…
On Utility and Privacy in Synthetic Genomic Data
Bristena Oprisanu, Georgi Ganev, Emiliano De Cristofaro
The availability of genomic data is essential to progress in biomedical research, personalized medicine, etc. However, its extreme sensitivity makes it problematic, if not outright…
Synthetic Data -- Anonymisation Groundhog Day
Theresa Stadler, Bristena Oprisanu, Carmela Troncoso
Synthetic data has been advertised as a silver-bullet solution to privacy-preserving data publishing that addresses the shortcomings of traditional anonymisation techniques. The pr…
How Much Does GenoGuard Really "Guard"? An Empirical Analysis of Long-Term Security for Genomic Data
Bristena Oprisanu, Christophe Dessimoz, Emiliano De Cristofaro
Due to its hereditary nature, genomic data is not only linked to its owner but to that of close relatives as well. As a result, its sensitivity does not really degrade over time; i…