Search Results for author:
Found 5 papers, 3 papers with code
CTAB-GAN+: Enhancing Tabular Data Synthesis
We extensively evaluate CTAB-GAN+ on data similarity and analysis utility against state-of-the-art tabular GANs.
Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data
And, while learning GANs to synthesize images on FL systems has just been demonstrated, it is unknown if GANs for tabular data can be learned from decentralized data sources.
Effective and Privacy preserving Tabular Data Synthesizing
Overall, it is found that DP-CTABGAN is capable of being robust to privacy attacks while maintaining the highest data utility as compared to prior work, by up to 18% in terms of the average precision score.
DTGAN: Differential Private Training for Tabular GANs
Additionally, we rigorously evaluate the theoretical privacy guarantees offered by DP empirically against membership and attribute inference attacks.
CTAB-GAN: Effective Table Data Synthesizing
In this paper, we develop CTAB-GAN, a novel conditional table GAN architecture that can effectively model diverse data types, including a mix of continuous and categorical variables.
