Data Fusion Technology

In the era of the digital economy, relying on the vigorous development of artificial intelligence, big data, and other industries, effective data fusion can bring huge economic and social benefits to the country, society, industries, and enterprises. Especially in the financial industry, the fusion of data from multiple parties and the use of big data and artificial intelligence have analyzed and assessed corporate and individual credit from multiple dimensions, which has played a pivotal role in preventing and managing credit and market risks, anti-fraud and anti-money laundering, and ensuring the financial security and market stability of China.

A variety of technologies have emerged to meet the needs for data security fusion. Secure Multi-Party Computation (MPC) and Federated Learning provide support to realize operations under data privacy.

MPC refers to collaboration computing completed securely with the joint participation of multiple parties without a trusted third party.

Reasonable use of MPC technology can break data barriers and connect data silos. It enables organizations in different industries to share data and ensure the integrity of shared data. It can specify the usage and amount used of data. Under the premise of ensuring data security and privacy, it can be reasonably and legally comply with the fusion of multi-party data for query and analysis, further contributing to the digitalization and intelligence of the financial industry.

Financial Technology Evaluation

National FinTech Evaluation Center (NFEC, also well-known as Bank Card Test Center or BCTC), as a national testing laboratory, applies professional strength in financial technology, continues to improve the evaluation service system of privacy computing financial application, and carries out evaluation services for secure multi-party computation financial applications.

The evaluation of MPC financial application includes evaluation of general-purpose MPC products and evaluation of special-purpose MPC products. General-purpose products are those that meet all functional indicators and security requirements of MPC and can be adapted to various financial application scenarios. Special-purpose products are products designed for specific financial application scenarios, which can achieve some functional indicators but meet all security requirements.

The testing specification contains four areas: basic features, technical capability, computing performance, and product security.

l  Basic features: MPC's product architecture, workflow, etc.

l  Technical capability: data types, algorithms, compilation capability, etc. supported by MPC.

l  Performance evaluation: multi-user concurrent, multi-task concurrent, and other scenarios supported by MPC.

l  Product security: data communication, data storage, result auditing, and evidence traceability, etc. supported by MPC

Based on developing MPC financial application product evaluation services, BCTC carries out privacy computing products evaluation services such as Federated Learning, Trusted Execution Environment, etc. The first batch of product tests for Federal Learning financial applications is in the final stage and will be announced soon. BCTC welcomes more companies to bring their FinTech privacy computing products to participate in the test and work together to ensure the stable development of the integrated application of financial data security.