Abstract: 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.

Abstract: Examples in this application disclose information sharing computer-implemented methods, media, and systems. One example computer-implemented method includes obtaining, from an external server at a trusted execution environment (TEE), a trigger instruction, in response to obtaining the trigger instruction, combining, at the TEE, first anti-money laundering (AML) risk information provided by a first institution and second AML risk information provided by a second institution to obtain third AML risk information, where the first AML risk information and the second AML risk information correspond to a user identifier, and sending, from the TEE to the first institution, the third AML risk information.

Abstract: Examples in this application disclose information sharing methods, media, and systems. One example computer-implemented method includes identifying, in a trusted execution environment (TEE), a transaction for invoking a smart contract, where the transaction is a first transaction initiated by a first institution or a second transaction initiated by a second institution, the first transaction comprise a first user identity of a first user and encrypted data of the first user, and the second transaction comprise a second user identity of a second user, updating accumulative invoking information for the smart contract based on the transaction, determining whether the updated accumulative invoking information satisfies an execution condition for the smart contract, executing the smart contract to obtain a user verification result to verify data of the second user, and sending the user verification result to the second institution.

Abstract: Examples in this application disclose data check computer-implemented methods, media, and systems. One example computer-implemented method includes retrieving, by a trusted execution environment (TEE), a check-triggering instruction from a server, where the check-triggering instruction is configured to trigger the TEE to perform a consistency check on basic data of a user to be identified, in response to the check-triggering instruction, retrieving, by the TEE, encrypted standard basic data of the user from a trusted institution, retrieving, by the TEE, encrypted basic data of the user from a first institution, retrieving, by the TEE, the basic data of the user by decrypting the encrypted basic data, performing, by the TEE, the consistency check on the basic data of the user based on the encrypted standard basic data to obtain a check result, and sending, from the TEE, the check result to a second institution.

Abstract: Examples in this application disclose an information sharing methods, media, and systems. One example method includes receiving, by a trusted execution environment (TEE), a user verification result acquisition request from a first institution, where the user verification result acquisition request is configured to request a verification result associated with a user, receiving first encrypted data of the user from a second institution, decrypting the first encrypted data to obtain first user basic data, receiving second encrypted data of the user from a trusted institution, decrypting the second encrypted data of the user to obtain second user basic data, in response to the user verification result acquisition request, determining the verification result of the user to indicate whether the first user basic data matches the second user basic data, and sending the verification result of the user to the first institution.

Abstract: Examples in this application disclose data verification methods, media, and systems. One example method includes receiving, from a blockchain network by a trusted execution environment (TEE) in a blockchain node, a trigger instruction based on a timed starting logic identifying a starting time to execute a smart contract, where the timed starting logic is comprised in a chain code which comprises information of a blockchain account and is executable by the blockchain node to determine the information of the blockchain account, retrieving first encrypted data from a first institution, decrypting the first encrypted data, receiving a data sharing request comprising a user identity corresponding to user basic data from a second institution, retrieving the user basic data based on the user identity, performing verification processing of the user basic data based on the trigger instruction to obtain a verification result, and sending the verification result to the second institution.

Abstract: Examples in this application disclose information sharing computer-implemented methods, media, and systems. One example computer-implemented method includes receiving, from a blockchain network by a trusted execution environment (TEE), a trigger instruction based on timed starting logic, where the timed starting logic is used to determine a starting moment of a smart contract, and the timed starting logic is defined in a chain code of the blockchain network. The example method further includes combining, by the TEE, first anti-money laundering (AML) risk information and second AML risk information based on the trigger instruction to obtain a combination result, where the first AML risk information is sent by a first institution for a first user ID of a user, the second AML risk information is sent by a second institution for the user. The example method yet further includes sending, by the TEE, the combination result to the first institution.

Abstract: Examples in this application disclose an information sharing methods, media, and systems. One example method includes receiving, by a trusted execution environment (TEE), a user verification result acquisition request from a first institution, where the user verification result acquisition request is configured to request a verification result associated with a user, receiving first encrypted data of the user from a second institution, decrypting the first encrypted data to obtain first user basic data, receiving second encrypted data of the user from a trusted institution, decrypting the second encrypted data of the user to obtain second user basic data, in response to the user verification result acquisition request, determining the verification result of the user to indicate whether the first user basic data matches the second user basic data, and sending the verification result of the user to the first institution.

Abstract: 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.

Abstract: Examples in this application disclose information sharing computer-implemented methods, media, and systems. One example computer-implemented method includes obtaining, from an external server at a trusted execution environment (TEE), a trigger instruction, in response to obtaining the trigger instruction, combining, at the TEE, first anti-money laundering (AML) risk information provided by a first institution and second AML risk information provided by a second institution to obtain third AML risk information, where the first AML risk information and the second AML risk information correspond to a user identifier, and sending, from the TEE to the first institution, the third AML risk information.

Abstract: Examples in this application disclose data check computer-implemented methods, media, and systems. One example computer-implemented method includes retrieving, by a trusted execution environment (TEE), a check-triggering instruction from a server, where the check-triggering instruction is configured to trigger the TEE to perform a consistency check on basic data of a user to be identified, in response to the check-triggering instruction, retrieving, by the TEE, encrypted standard basic data of the user from a trusted institution, retrieving, by the TEE, encrypted basic data of the user from a first institution, retrieving, by the TEE, the basic data of the user by decrypting the encrypted basic data, performing, by the TEE, the consistency check on the basic data of the user based on the encrypted standard basic data to obtain a check result, and sending, from the TEE, the check result to a second institution.

Abstract: Examples in this application disclose information sharing computer-implemented methods, media, and systems.

Abstract: Examples in this application disclose data verification methods, media, and systems. One example method includes receiving, from a blockchain network by a trusted execution environment (TEE) in a blockchain node, a trigger instruction based on a timed starting logic identifying a starting time to execute a smart contract, where the timed starting logic is comprised in a chain code which comprises information of a blockchain account and is executable by the blockchain node to determine the information of the blockchain account, retrieving first encrypted data from a first institution, decrypting the first encrypted data, receiving a data sharing request comprising a user identity corresponding to user basic data from a second institution, retrieving the user basic data based on the user identity, performing verification processing of the user basic data based on the trigger instruction to obtain a verification result, and sending the verification result to the second institution.

Abstract: Examples in this application disclose information sharing methods, media, and systems. One example computer-implemented method includes identifying, in a trusted execution environment (TEE), a transaction for invoking a smart contract, where the transaction is a first transaction initiated by a first institution or a second transaction initiated by a second institution, the first transaction comprise a first user identity of a first user and encrypted data of the first user, and the second transaction comprise a second user identity of a second user, updating accumulative invoking information for the smart contract based on the transaction, determining whether the updated accumulative invoking information satisfies an execution condition for the smart contract, executing the smart contract to obtain a user verification result to verify data of the second user, and sending the user verification result to the second institution.

Abstract: A method for risk feature screening comprises: acquiring respective feature weights of a plurality of risk features, wherein the feature weights are either obtained by using a classification model trained using sample events or predefined, and wherein the classification model is configured to determine risk events; and selecting at least a part of the plurality of risk features through screening according to the feature weights and a predetermined constraint for limiting the length of a message generated based on the risk features.