Interpretation of the Shared Intelligence Standard and Its Practical Applications
This article explains the classification, value, and detailed interpretation of the first national shared intelligence standard, describes its technical background and solutions such as Trusted Execution Environments and Secure Multi‑Party Computation, and showcases real‑world applications and industry recognitions.
The Ant Group recently launched a series of online live lectures titled “Fighting the Pandemic with Technology Breakthroughs,” and the full transcript of the shared‑intelligence standard presentation is provided here.
The speaker, a senior standardization engineer, introduces the newly released Shared Intelligence Alliance Standard, the first of its kind in China, and outlines its classification by hierarchy, attribute, object, and nature, emphasizing its role in guiding technology, shaping market rules, focusing industry standards, and fostering ecosystem development.
The standard’s scope includes technical requirements, implementation guidelines, evaluation criteria, management, foundational protocols, and innovative research, with Ant Group contributing heavily to technical‑requirement and innovation‑research categories.
Key motivations for the standard stem from data‑island challenges, privacy concerns, and regulatory pressures such as GDPR, prompting the need for frameworks that enable secure, multi‑party data collaboration while preserving privacy.
Two primary technical solutions are covered: Trusted Execution Environment (TEE) and Secure Multi‑Party Computation (MPC). TEE relies on hardware‑based secure enclaves to encrypt and process data centrally, while MPC uses cryptographic protocols to allow distributed learning without exposing raw data.
The standard also defines the technical framework for shared‑learning systems, including architecture diagrams, functional components, security requirements, and example scenarios like intelligent risk control and marketing.
Practical deployments are highlighted, such as joint credit risk modeling with Jiangsu Bank using TEE, which improves model accuracy and reduces losses, and the broader application of both TEE and MPC in anti‑fraud and risk‑control networks.
The initiative has earned multiple industry awards, including the Zijin Product Innovation Award, AI Application Case Demonstration Award, and CCF Outstanding Scientific Progress Award.
Finally, the speaker invites external partners to join the standard‑co‑creation effort to further advance shared‑intelligence technologies.
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