CHINA UNIONPAY CO., LTD.

Provided in the embodiments of the present application are a consortium blockchain admission management method and apparatus. The method includes: for each first-level node which has a direct access control permission for a basic admission management component, in response to a hosting transaction, which is initiated by the first-level node toward the basic admission management component, the basic admission management component constructing a corresponding second-level institution contract; and when the second-level institution contract is in a cooperation mode, in response to a node admission transaction, which is initiated by an initial second-level node toward the second-level institution contract, adjusting second-level nodes, wherein each second-level node has an indirect access control permission for the basic admission management component and can join a blockchain network, and the second-level institution contract realizes multi-level node admission management by means of multi-level expansion.

The present application discloses an object matching method, apparatus, device, system, medium and program product. The method includes: selecting one of first feature vectors of first objects in a first device as a first target feature vector, and calculating a first similarity parameter between the first target feature vector and each of the first feature vectors; obtaining, based on the first target feature vector and a second feature vector of a second object in a second device, a second similarity parameter between the second feature vector and the first target feature vector by a vector similarity calculation method with privacy protection; selecting a first feature vector to be matched according to the first similarity parameter, the second similarity parameter, and a similarity matching threshold; and determining, based on the first feature vector to be matched and the second feature vector, the first object successfully matched with the second object according to the similarity matching threshold by the vector similarity calculation method with privacy protection.

The present application discloses a method for comparing service parameters, device, system, and storage medium, belonging to the field of big data processing. The method includes: obtaining a first interaction string and a first result string based on a first binary string converted from a first service parameter of a first device, randomly generated first label strings, and agreed initial random strings; sending the first interaction string to a second device, so that the second device obtains a second result string based on a second binary string converted from a second service parameter of the second device, the initial random strings, part of randomly generated second label strings, and the first interaction string; and interacting with the second device based on the first result string and the second result string for fragmented computation to obtain a first comparison result.

The present application discloses a method for selecting a model input feature, apparatus, device, and storage medium, and relates to the field of big data processing. The method comprises: acquiring a candidate feature, feature data comprising the candidate feature, and an original binning point of an original bin for a value of the candidate feature; constructing a critical bin based on the original binning point; obtaining a first model input performance index according to the value of the candidate feature in the feature data and the critical bin, wherein the first model input performance index comprises a model input performance index of the candidate feature in the critical bin; and selecting the model input feature from the candidate feature according to the first model input performance index of the candidate feature and a pre-acquired second model input performance index of the candidate feature.

A method for training a quantum neural network and a method for classifying data are disclosed in the present disclosure. The method for training the quantum neural network includes acquiring sample data and a sample category label corresponding to the sample data, performing feature extraction on the sample data using a feature extraction layer of the quantum neural network, inputting the sample feature obtained by extracting into a unitary matrix layer to obtain a unitary matrix corresponding to the sample feature, adjusting a quantum state of a first qubit based on the unitary matrix to obtain a second qubit, the quantum state of the first qubit corresponds to the sample category label; determining a quantum state fidelity between the second qubit and the first qubit using the quantum circuit and then determining a loss value.

The present application belongs to the field of new generation of information technology. Disclosed are a digital payment processing method and apparatus, and a device, a system and a medium. The method comprises: by means of a payment routing gateway device, receiving a digital seal and an order key element signature, which are sent by a payment information service provider service platform, wherein the digital seal and the order key element signature are generated by the payment information service provider service platform according to order information and a digital credential, the digital credential is generated by a digital credential platform and comprises static account data ciphertext and account carrier information, and the static account data ciphertext is obtained by performing encryption according to static account data; verifying the digital seal and the order key element signature to obtain a transaction element signature.

The disclosure provides a a privacy computing method, including that, when first computing data is obtained after corresponding computation is performed on the basis of a first sub-algorithm in a privacy algorithm on to-be-processed data, and when it is determined that the first computing data needs to be recomputed on the basis of a second sub-algorithm in the privacy algorithm, where the second sub-algorithm is stored in another privacy computing device, a proxy API corresponding to the first sub-algorithm and a proxy API corresponding to the second sub-algorithm are determined according to a pre-stored correspodance between sub-algorithms and proxy APIs. The first computing data is sent, by means of the proxy API corresponding to the first sub-algorithm, to the proxy API corresponding to the second sub-algorithm, such that the other privacy computing device performs corresponding computation on the first computing data based on the second sub-algorithm.

The present application discloses a sensitive data identification method and apparatus, a device, and a computer storage medium. A text mining technology is used to mine a plurality of sensitive data rules from a data security specification file of a target industry to form a sensitive data rule base, the rule base is continuously augmented by using technologies such as NLP and NER, and after data to be identified of the target industry is obtained, a sensitivity class and a sensitivity level of the data to be identified can be identified by matching the sensitive data rules in the sensitive data rule base corresponding to the target industry with the data to be identified.

Disclosed in the present disclosure are a data matching method. In the present disclosure, a first vector corresponding to first data and a second vector corresponding to second data are respectively obtained; a first encrypted vector obtained by means of encrypting the first vector and a second encrypted vector obtained by means of encrypting the second vector are acquired; a first encrypted distance is calculated on the basis of the first encrypted vector and the second encrypted vector; a target distance between the first vector and the second vector is determined on the basis of the first encrypted distance and a first target private key; and on the basis of the target distance and a first preset distance threshold value, it is determined whether the first data matches the second data.

The disclosure describes a data sharing system, method, apparatus, a device and a medium. In the data sharing method, a device to which a first application belongs determines, according to a pre-stored correspondence between application identifiers and access addresses, a target access address corresponding to a target application identifier of a second application, and sends to the target access address a sharing request for sharing data of the second application, where the sharing request carries the target application identifier; and a device to which the target access address belongs obtains data stored in a target database, determines a target encryption key corresponding to the target application identifier, decrypts the obtained data according to the target encryption key, and sends the decrypted data to the device to which the first application belongs.