Data Security Computing Platform

TEE as a technical basis to ensure that enterprise data is available and invisible

Related Background

The 14th Five-Year Plan proposes to accelerate the construction of digital economy, digital society and digital government, build digital China and create new advantages in digital economy, making clear the importance of data as a core production factor. In the data era, on the one hand, the country wants to build a digital economy and society, and support data open sharing and interconnection; but on the other hand, the security issues brought by data open sharing have to be paid attention to


Trustkernel privacy computing platform is developed by the Trustkernel independent privacy data computing platform, based on the Trustkernel in the TEE trusted execution environment of continuous research and exploration, truly realize the privacy data "available invisible". It provides a powerful computing platform and capability for enterprises to securely access and manage the data in the work area, activating the greater value brought by data aggregation and achieving 1+1>2.

Key Features

Quick Bulk Import

Automatic import of multiple relationship models, fast and efficient

Diversified Integration

Support data source association, eliminate data silos, and explore data value

High-speed computing

Lower loss of cryptographic operations in TEE environment

Standard Output

Standardized output data, refined data management

Application Cases



Water digital transformation and upgrading construction by the physical world within the sensor, smart pumps, smart valves and other various equipment data collection, the use of Internet of Things technology and cloud computing. The production and operation of the physical world into the digital world of real-time data and visual graphical display, for the digital initiative of the full sense, online, intelligent, according to the full channel to provide basic data and basic design.


With the help of privacy computing, we can import multi-dimensional data from consumption data, traffic and travel data into DataCube's risk control model without disclosing user information. Using the big data risk control model to predict the borrower's score and do intelligent risk control to do comprehensive credit rating of customers, reduce the risk of borrowing, reduce the occurrence of overdue and bad debt problems, thus reducing operating costs and increasing revenue.