Financial Technology Testing Facilitates Secure Data Sharing in Finance

The rapid development of data as a production factor and new regulations have created a demand for secure financial data sharing, prompting the integration of privacy‑computing techniques such as multi‑party computation (MPC), federated learning, trusted execution environments, and blockchain to address data isolation and compliance challenges.

Key technical routes include a "3+1" model—MPC, federated learning, trusted execution environments, plus blockchain for immutable audit trails—each offering distinct security guarantees and compatibility considerations for financial applications like joint queries, risk assessment, and fraud detection.

National policies and industry guidelines (e.g., PBOC’s FinTech development plan, data governance directives) have progressively refined standards for data classification, lifecycle security, and privacy protection, providing a regulatory backbone for product evaluation.

Financial technology testing, conducted by the Shenzhen National FinTech Evaluation Center, assesses products against these standards across four dimensions: basic requirements, security requirements, performance requirements, and optional items, issuing certificates that support certification under the central‑bank‑led FinTech product catalog.

Specific evaluation tracks cover MPC, federated learning, blockchain, and privacy‑computing appliances, examining roles, workflow, cryptographic guarantees, protocol security, communication security, logging, and performance metrics such as latency, throughput, and precision.

The concluding insight emphasizes that standardization and rigorous testing are essential to safely unlock the value of data sharing in finance, with the evaluation body aiming to provide professional, user‑centric services that align with national strategies, regulatory expectations, and industry growth.