PrIeD-KIE: Towards Privacy Preserved Document Key Information Extraction
Saifullah Saifullah Stefan Agne Andreas Dengel Sheraz Ahmed
In this paper, we introduce strategies for developing private Key Information
Extraction (KIE) systems by leveraging large pretrained document foundation
models in conjunction with differential privacy (DP), federated learning (FL),
and Differentially Private Federated Learning (DP-FL). Through extensive
experimentation on six benchmark datasets (FUNSD, CORD, SROIE, WildReceipts,
XFUND, and DOCILE), we demonstrate that large document foundation models can be
effectively fine-tuned for the KIE task under private settings to achieve
adequate performance while maintaining strong privacy guarantees. Moreover, by
thoroughly analyzing the impact of various training and model parameters on
model performance, we propose simple yet effective guidelines for achieving an
optimal privacy-utility trade-off for the KIE task under global DP. Finally, we
introduce FeAm-DP, a novel DP-FL algorithm that enables efficiently upscaling
global DP from a standalone context to a multi-client federated environment. We
conduct a comprehensive evaluation of the algorithm across various client and
privacy settings, and demonstrate its capability to achieve comparable
performance and privacy guarantees to standalone DP, even when accommodating an
increasing number of participating clients. Overall, our study offers valuable
insights into the development of private KIE systems, and highlights the
potential of document foundation models for privacy-preserved Document AI
applications. To the best of authors' knowledge, this is the first work that
explores privacy preserved document KIE using document foundation models.