公开(公告)号：US20210027145A1

公开(公告)日：2021-01-28

申请号：US17040144

申请日：2019-03-19

Applicant: CHINA UNIONPAY CO., LTD.

Inventor： Xurui LI ,  Jianbin ZHENG ,  Jintao ZHAO

IPC: G06N3/04 ,  G06K9/62 ,  G06N3/063 ,  G06N5/00 ,  G06Q20/40

Abstract: A fraudulent transaction detection method comprises: performing feature mapping processing on each of a plurality of transaction data to generate corresponding feature vectors; converting the feature vectors of a transaction to be detected into integrated feature vectors based on a first self-learning model; respectively converting the feature vectors respectively of at least one time sequence transaction into time sequence feature vectors based on a second self-learning model; combining the integrated feature vectors and each of the time sequence feature vectors corresponding to each of the time sequence transactions to form depth feature vectors; classifying the depth feature vectors based on a third self-learning model to determine whether the transaction to be detected is a normal transaction or a fraudulent transaction.

公开(公告)号：US20240323023A1

公开(公告)日：2024-09-26

申请号：US18579216

申请日：2022-07-20

Applicant: CHINA UNIONPAY CO., LTD.

Inventor： Yongkai ZHOU ,  Yanming YANG ,  Hongbao LIU ,  Jianbin ZHENG ,  Yang YANG ,  Zhenyao QIU ,  Chen OUYANG ,  Ling JIN ,  Pengfei GAO ,  Dong CHENG ,  Fen ZOU ,  Hang YUAN ,  Qi WANG

IPC: H04L9/32 ,  G06F21/60

CPC classification number: H04L9/3234 ,  G06F21/602

Abstract: A method for sample alignment is applied to a first participant system, where a first trusted execution environment is deployed at the first participant system. The method includes, in the first trusted execution environment, obtaining at least one first sample identifier of the first participant system; through the first trusted execution environment, obtaining at least one second sample identifier of the second participant system from the second trusted execution environment, where the second trusted execution environment is deployed at the second participant system; in the first trusted execution environment, determining the first initial intersection of the at least one first sample identifier and the at least one second sample identifier and performing the shuffle processing on all first target sample identifiers in the first initial intersection to obtain the first target intersection; and based on the first target intersection, determining the first sample alignment result.