BDSP: A Fair Blockchain-enabled Framework for Privacy-Enhanced Enterprise Data Sharing

TL;DR

This paper introduces BDSP, a blockchain-based federated learning framework that enhances privacy, transparency, and efficiency in enterprise data sharing, achieving higher accuracy and lower communication costs.

Contribution

The paper presents a novel framework combining blockchain and federated learning with a contribution accounting mechanism for fair data valuation.

Findings

Over 5% increase in training accuracy

Reduced communication overhead by 3 times

Effective data valuation and transparency mechanism

Abstract

Across industries, there is an ever-increasing rate of data sharing for collaboration and innovation between organizations and their customers, partners, suppliers, and internal teams. However, many enterprises are restricted from freely sharing data due to regulatory restrictions across different regions, performance issues in moving large volume data, or requirements to maintain autonomy. In such situations, the enterprise can benefit from the concept of federated learning, in which machine learning models are constructed at various geographic sites. In this paper, we introduce a general framework, namely BDSP, to share data among enterprises based on Blockchain and federated learning techniques. Specifically, we propose a transparency contribution accounting mechanism to estimate the valuation of data and implement a proof-of-concept for further evaluation. The extensive experimental results show that the proposed BDSP has a competitive performance with higher training accuracy, an increase of over 5%, and lower communication overhead, reducing 3 times, compared to baseline approaches.