Privacy Computing Unit for Secure AML Data Sharing
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Solution Overview
Problem
Current anti-money laundering (AML) systems face challenges in accurately sharing and combining AML risk information across different financial institutions, leading to inconsistent STR crime labels and money-laundering risk levels for the same users, which hampers effective AML audit capabilities.
Innovation Solution
An information sharing method and system utilizing a privacy computing unit to match user IDs and combine AML risk information from multiple institutions, leveraging blockchain technology for secure and accurate data sharing, while ensuring privacy protection through trusted execution environments and decentralized identity services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AML risk information is shared across multiple financial institutions, then the accuracy of AML audit capability is improved, but data privacy and security risks increase
Solution Approach 1:
The patent introduces a privacy computing unit as an intermediary between financial institutions. This unit performs secure multi-party computation to match user IDs and combine AML risk information without exposing raw data. The privacy computing unit acts as a trusted mediator that enables accurate AML auditing while protecting data privacy through cryptographic techniques and trusted execution environments.
2Measurement precision
If AML risk information from multiple institutions is combined, then comprehensive risk assessment is improved, but system complexity increases
Solution Approach 1:
The privacy computing unit serves as a centralized coordinator that simplifies the overall system architecture. Instead of requiring direct peer-to-peer connections between multiple institutions, the privacy computing unit centralizes the matching and combination operations, reducing communication overhead and system complexity while enabling comprehensive risk assessment.
Solution Approach 2:
The system divides functionality into distinct modules: institutions submit data, the privacy computing unit performs matching and combination, and results are returned to participants. This segmentation allows each component to be optimized independently and facilitates easier maintenance and scaling of the system.
3Stability of the object's composition
If user ID matching is performed across institutions, then consistent identification is improved, but computational overhead increases
Solution Approach 1:
The system performs user ID matching as a preliminary step before combining AML risk information. By pre-matching user IDs across institutions, the system establishes consistent identification early in the process, avoiding redundant computational work later and enabling efficient data combination only for matched records.
Data Source
AI summary
Examples in this application disclose information sharing methods, media, and systems. One example computer-implemented method includes receiving, by a trusted execution environment (TEE), a first sharing request from a first institution and a second sharing request from a second institution, where the first sharing request comprises a user identity of a first user and first anti-money laundering (AML) risk information and the second sharing request comprises a user identity of a second user and second AML risk information, comparing the user identity of the first user with the user identity of the second user, in response to that the user identity of the first user is the same as the user identity of the second user, combining the first AML risk information and the second AML risk information, and sending the combined first AML risk information and second AML risk information to the first institution and the second institution.