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Anti-fraud identification system and method based on cross-domain data analysis

A data analysis and identification system technology, applied in the field of security computing, can solve problems such as low equipment performance, anti-fraud, and narrow application range of federated learning, and achieve low equipment performance, efficient and accurate computing power

Pending Publication Date: 2022-04-19
INSPUR TIANYUAN COMM INFORMATION SYST CO LTD
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Problems solved by technology

[0003] The present invention provides an anti-fraud identification system and method based on cross-domain data analysis, which is used to solve the defects in the prior art that cannot be applied to cross-domain data anti-fraud, and realizes that the requirements for equipment performance are relatively low, and the structure and algorithm It is said to have its advanced nature and portability, and expand the disadvantages of the narrow application range of federated learning

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Embodiment Construction

[0050]In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention , but not all examples. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0051] Anti-fraud solutions in the financial field currently seldom take into account the combination of cross-domain or multi-domain data, and still prefer traditional machine learning to build recommendation algorithms, which mainly use feature values ​​to construct user portraits and behavior probability predictions to infer customer behavior. Multi-party secure computing is combining cross...

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Abstract

The invention provides an anti-fraud identification system and method based on cross-domain data analysis, and relates to the technical field of security computing, the system comprises at least two participating nodes and a hub node, the participating nodes are connected with each other, and the hub node is embedded in one of the participating nodes; the hub node is used for coordinating all the participating nodes for connection and issuing tasks to the participating nodes; wherein the task starts to be executed from the participation node embedded with the hub node, and after all the participation nodes participating in the task are traversed in sequence, the participation node embedded with the hub node carries out decryption, and then the task is executed. And the hub node broadcasts an anti-fraud identification result of the task to all the participating nodes participating in the task. The method is low in requirement on equipment performance, has advancement and portability from the aspects of structure and algorithm, and overcomes the defect that the application range of federal learning is narrow.

Description

technical field [0001] The invention relates to the technical field of secure computing, in particular to an anti-fraud identification system and method based on cross-domain data analysis. Background technique [0002] At present, there are two ways to prevent fraud and prevent user privacy leakage through joint cross-domain data: the first is to send an encrypted ID acquisition request based on the encryption server, and the ID encryption and decryption server returns the encrypted ID after receiving the above request. However, the first solution has higher requirements for hardware, and has no obvious protective effect on cross-domain data, especially banking and insurance industry data with the characteristics of data islands; the second solution is for banking and operations The data on the business side is modeled by completing customer portraits, but the second scheme is mainly applied to vertical federated learning. Vertical federated learning is to splicing two data...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F21/57G06F21/62G06F9/54G06N20/00
CPCG06F21/57G06F21/6245G06F9/542G06N20/00
Inventor 李尚锴王凯袁明明
Owner INSPUR TIANYUAN COMM INFORMATION SYST CO LTD
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