80 citations · 125 across the 13 of their papers we have counts for
13 papers
Privacy Preserving Diffusion Models for Mixed-Type Tabular Data Generation
Timur Sattarov, Marco Schreyer, Damian Borth
We introduce DP-FinDiff, a differentially private diffusion framework for synthesizing mixed-type tabular data. DP-FinDiff employs embedding-based representations for categorical f…
Diffusion-Scheduled Denoising Autoencoders for Anomaly Detection in Tabular Data
Timur Sattarov, Marco Schreyer, Damian Borth
Anomaly detection in tabular data remains challenging due to complex feature interactions and the scarcity of anomalous examples. Denoising autoencoders rely on fixed-magnitude noi…
Federated Diffusion Modeling with Differential Privacy for Tabular Data Synthesis
Timur Sattarov, Marco Schreyer, Damian Borth
The increasing demand for privacy-preserving data analytics in various domains necessitates solutions for synthetic data generation that rigorously uphold privacy standards. We int…
FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation
Timur Sattarov, Marco Schreyer, Damian Borth
Realistic synthetic tabular data generation encounters significant challenges in preserving privacy, especially when dealing with sensitive information in domains like finance and…
FinDiff: Diffusion Models for Financial Tabular Data Generation
Timur Sattarov, Marco Schreyer, Damian Borth
The sharing of microdata, such as fund holdings and derivative instruments, by regulatory institutions presents a unique challenge due to strict data confidentiality and privacy re…
Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data
Timur Sattarov, Dayananda Herurkar, Jörn Hees
Recent advances in Explainable AI (XAI) increased the demand for deployment of safe and interpretable AI models in various industry sectors. Despite the latest success of deep neur…