PrIeD-KIE: Towards Privacy Preserved Document Key Information Extraction

TL;DR

This paper explores privacy-preserving key information extraction from documents using large foundation models combined with differential privacy and federated learning, introducing a new DP-FL algorithm and practical guidelines.

Contribution

It introduces FeAm-DP, a novel DP-FL algorithm for scalable privacy-preserving document KIE, and provides practical guidelines for balancing privacy and utility.

Findings

Large document foundation models can be effectively fine-tuned under privacy constraints.

FeAm-DP achieves comparable performance to standalone DP in federated settings.

The study offers practical guidelines for privacy-utility trade-offs in private KIE.

Abstract

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.