FLAIM: AIM-based Synthetic Data Generation in the Federated Setting

arXiv:2310.03447v2 Announce Type: replace
Abstract: Preserving individual privacy while enabling collaborative data sharing is crucial for organizations. Synthetic data generation is one solution, producing artificial data that mirrors the statistical properties of private data. While numerous techniques have been devised under differential privacy, they predominantly assume data is centralized. However, data is often distributed across multiple clients in a federated manner. In this work, we initiate the study of federated synthetic tabular data generation. Building upon a SOTA central method …

aim artificial arxiv cs.cr cs.lg data data sharing differential privacy federated organizations privacy private private data producing sharing solution synthetic synthetic data techniques under