A Methodology of Trusted Data Sharing across Telecom and Finance Sector under China’s Data Security Policy

Abstract

Data security policies have significant impacts on big data and artificial intelligence applications, and yield data silo due to the data inaccessibility among the commercial companies for privacy-preserving. On September 1st, 2021, China officially implemented the Data Security Law. This paper timely proposes, validates, and productizes a trusted data sharing solution based on Vertical Federated Learning (VFL) technology across telecom and finance companies. A VFL model with Hetero Secure Boost Tree (HSBT) algorithm is proposed to overcome the data silo problems and to preserve user data privacy. Experimental results demonstrate that the model with both financial and telecom data improves the success rate of marketing for a tier-1 commercial bank in China. The solution has also been successfully implemented to greatly improve the marketing efficiency, resulting an approximately 50% cost reduction.