A Blockchain-Enhanced Reversible Watermarking Framework for End-to-End Data Traceability in Federated Learning Systems

In federated learning (FL) environments, ensuring data traceability presents significant challenges, particularly when data move between multiple entities such as data centers, edge nodes, and data scientists. This paper presents a novel framework that combines robust reversible watermarking and blockchain technology to achieve end-to-end traceability of medical images in a FL context. Based on the watermark, it becomes possible to interrogate the blockchain about the life cycle of an image to ensure data traceability, authenticity, and integrity. We use a histogram shifting-based reversible watermarking scheme with a new overflow management procedure, integrated with a private blockchain that records all watermarking and verification operations. Experimental results demonstrate the effectiveness of our approach in terms of watermark robustness considering a chest X-ray image dataset. We further show that watermarking does not interfere in the training and inference phase of a VGG-16 classification model for a Covid-19 medical database. A model trained on protected data can be used to classify non-watermarked data as well.