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

arXiv:2310.03447v2 Announce Type: replace-cross
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 …

abstract aim artificial arxiv collaborative cs.cr cs.lg data data sharing differential differential privacy enabling organizations privacy private data solution statistical synthetic synthetic data type