9 citations · 31 across the 18 of their papers we have counts for
4 papers · 1 filter
The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasets
Georgi Ganev, Emiliano De Cristofaro
Generative models producing synthetic data are meant to provide a privacy-friendly approach to releasing data. However, their privacy guarantees are only considered robust when mod…
On the Challenges of Deploying Privacy-Preserving Synthetic Data in the Enterprise
Lauren Arthur, Jason Costello, Jonathan Hardy +4
Generative AI technologies are gaining unprecedented popularity, causing a mix of excitement and apprehension through their remarkable capabilities. In this paper, we study the cha…
When Synthetic Data Met Regulation
Georgi Ganev
In this paper, we argue that synthetic data produced by Differentially Private generative models can be sufficiently anonymized and, therefore, anonymous data and regulatory compli…
Graphical vs. Deep Generative Models: Measuring the Impact of Differentially Private Mechanisms and Budgets on Utility
Georgi Ganev, Kai Xu, Emiliano De Cristofaro
Generative models trained with Differential Privacy (DP) can produce synthetic data while reducing privacy risks. However, navigating their privacy-utility tradeoffs makes finding…