Privacy-Aware Document Visual Question Answering
Rubèn Tito Khanh Nguyen Marlon Tobaben Raouf Kerkouche Mohamed Ali Souibgui Kangsoo Jung Lei Kang Ernest Valveny Antti Honkela Mario Fritz Dimosthenis Karatzas
Document Visual Question Answering (DocVQA) is a fast growing branch of
document understanding. Despite the fact that documents contain sensitive or
copyrighted information, none of the current DocVQA methods offers strong
privacy guarantees.
  In this work, we explore privacy in the domain of DocVQA for the first time.
We highlight privacy issues in state of the art multi-modal LLM models used for
DocVQA, and explore possible solutions.
  Specifically, we focus on the invoice processing use case as a realistic,
widely used scenario for document understanding, and propose a large scale
DocVQA dataset comprising invoice documents and associated questions and
answers. We employ a federated learning scheme, that reflects the real-life
distribution of documents in different businesses, and we explore the use case
where the ID of the invoice issuer is the sensitive information to be
protected.
  We demonstrate that non-private models tend to memorise, behaviour that can
lead to exposing private information. We then evaluate baseline training
schemes employing federated learning and differential privacy in this
multi-modal scenario, where the sensitive information might be exposed through
any of the two input modalities: vision (document image) or language (OCR
tokens).
  Finally, we design an attack exploiting the memorisation effect of the model,
and demonstrate its effectiveness in probing different DocVQA models.