9 citations · 31 across the 18 of their papers we have counts for
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cs.LG2021
DP-SGD vs PATE: Which Has Less Disparate Impact on GANs?
Georgi Ganev
Generative Adversarial Networks (GANs) are among the most popular approaches to generate synthetic data, especially images, for data sharing purposes. Given the vital importance of…
cs.LG2021★ 9 cited
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…
q-bio.GN2021★ 2 cited
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…