Abstract: Constructing knowledge graph data is described. Full data of a knowledge graph at a specified time is obtained. Incremental data of the full data is calculated relative to full data obtained at a previous time granularity. A version number of the incremental data is generated at least partially based on the specified time. By using a protocol and as a dynamic mapping, a node and an edge of the knowledge graph that are associated with the incremental data are dynamically mapped. The incremental data with reference to the version number is stored as stored incremental data.

Abstract: Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage media, for managing blockchain-based trustable transaction services. One of the methods includes: storing order data of an order between a buyer and a seller on a blockchain of a blockchain network, the order data including one or more payment conditions, verifying that the buyer has a trustable automatic payment service guaranteed by a buyer financial institution, generating a smart contract for the order based on the order data on the blockchain, and executing the smart contract to automatically instruct the buyer financial institution to make a payment of the order for the buyer to the seller according to the trustable automatic payment service in response to determining that a payment condition for the payment specified in the smart contract is met based on order status data uploaded to the blockchain.

Abstract: Embodiments of this specification disclose computer-implemented methods, apparatuses, systems, mediums, and program products related to batch encryption. In an example computer-implemented method, N first plaintexts are obtained. The N first plaintexts are spliced based on a first predetermined rule to obtain a first target plaintext. The first target plaintext are encrypted by using a predetermined encryption algorithm to obtain a first target ciphertext. N is a positive integer greater than or equal to 2.

Abstract: Computer-implemented methods, apparatuses, storage media, and system are disclosed. In an example, a first terminal device sends first encrypted data to a second terminal device. The second terminal device encrypts the first encrypted data by using a second public key to obtain second encrypted data, and sends the second encrypted data and third encrypted data to the first terminal device. The first terminal device encrypts the third encrypted data by using a first public key to obtain fourth encrypted data, acquires first intersection data of the second encrypted data and the fourth encrypted data, and sends the first intersection data to a third terminal device. The third terminal device acquires fifth encrypted data from the first terminal device, determines second intersection data based on the fifth encrypted data and the first intersection data, and sends the second intersection data to the first terminal device and the second terminal device.

Abstract: One or more embodiments of this specification discloses vehicle control. An implementation includes performing a first control operation corresponding to a first parameter threshold on a target vehicle when it is detected that a first parameter between an electronic device and the target vehicle meets a triggering condition for the first parameter threshold corresponding to the target vehicle. A second parameter threshold corresponding to the target vehicle is adjusted from an initial threshold to a target threshold to increase a difference between the first parameter threshold and the second parameter threshold. A second control operation corresponding to the second parameter threshold on the target vehicle is performed when it is detected that the first parameter meets a triggering condition for the target threshold.

Abstract: Implementations disclose data processing methods, apparatuses, and computer devices for privacy protection in secure multi-party computation, including encoding private data to a coefficient of a first polynomial function. A plurality of function values of the first polynomial function are obtained as a plurality of fragments obtained after the private data is split, where the fragments of the private data are used for computation by using a secret sharing algorithm to obtain fragments of target data.

Abstract: Embodiments of this specification provide computer-implemented methods, apparatuses, and systems for training a model using multiple data owners. In an example method, a second data owner determines, according to first data, second feature data intersecting the first data. The following main iteration process is performed until an iteration end condition is met: performing, for each training unit by using a first training sample and a second training sample, cooperative training on a first model, a second model, and a third model that participate in training of the training unit. A master server performs federated aggregation on the trained first model and/or third model in each training unit, to obtain a corresponding first global model and/or third global model. The first model is updated according to the first global model and/or the third model is updated according to the third global model.

Abstract: Implementations include obtaining data volume information indicating an amount of data used by a participating node to train, based on local data, a basic training model, and where the local data includes user data of a target organization corresponding to the participating node. Based on the local data and by the participating node, obtaining a node local gradient by training the basic training model. Based on the data volume information and the node local gradient, determining a global gradient of a federated learning model that the participating node participates in. Based on the node local gradient of the participating node and the global gradient, determine a degree of participation of the participating node, where the degree of participation indicates a degree of participation of the participating node in federated learning model training. Based on the degree of participation, determine an actual model gradient of the participating node.

Abstract: Embodiments of this specification provide computer-implemented methods, apparatuses, computer-readable media and systems for secure multi-party computation. According to an example computer-implemented method, a first party performs a first mapping operation and homomorphic encryption on first plaintext data to obtain a first converted ciphertext in a Montgomery state, where the first mapping operation converts data from an integer ring to the Montgomery state. The first party sends the first converted ciphertext to a second party. Then, the second party performs a first homomorphic operation in the Montgomery state based on the first converted ciphertext to obtain a first result ciphertext in the Montgomery state, where the first homomorphic operation includes a modular multiplication operation.

Abstract: Embodiments of this specification provide computer-implemented methods, apparatuses, computer-readable media, and systems for secure multi-party computation. In an example secure multi-party computation method, a first party encrypts a first plaintext segment of target data by using a homomorphic encryption algorithm based on a public key held by the first party in a first key pair to obtain a first ciphertext segment. A second plaintext segment of the target data is owned by a second party. The first party sends the first ciphertext segment to the second party. The second party performs a homomorphic addition operation in the homomorphic encryption algorithm on the first ciphertext segment and the second plaintext segment of the target data to obtain ciphertext data of the target data. The ciphertext data is decrypted based on a private key in the first key pair.

Abstract: A first party of two parties performs masking on three first-party fragments corresponding to three types of training data split into fragments and distributed between the two parties by using first fragments of three random numbers in a first fragment of a random array to obtain three first mask fragments sent to a second party, the first fragment of the random array is a fragment, sent by a third party to the first party, of two-party fragments obtained by splitting values in the random array generated by the third party. Three pieces of mask data are constructed by using the three first mask fragments and three second mask fragments received from the second party. A first calculation based on the three pieces of mask data and the first fragment of the random array is performed to obtain a first gradient fragment for updating the first-party fragment of the model parameter.

Abstract: Computer-implemented methods, apparatuses, and systems for transferring a transaction using a blockchain integrated station are provided. The blockchain integrated station includes a central processing unit (CPU) and a smart network card. The smart network card is configured to receive a transaction of a blockchain network, wherein the blockchain integrated station is a blockchain node of the blockchain network, and determine other blockchain nodes in the blockchain network; and in response to determining the other blockchain nodes of the blockchain network, transfer the transaction to the other blockchain nodes of the blockchain network.

Abstract: Embodiments of the present specification disclose methods, apparatuses, and devices for transferring data assets based on a blockchain. One method includes: obtaining usage demand information of a data requestor for using original data, wherein the usage demand information indicates a demand of the data requestor for performing computations based on the original data; sending a usage authorization request for the original data to a data owner of the original data based on a decentralized identifier (DID) corresponding to the original data; receiving confirmation information of the usage authorization request from the data owner; processing the original data based on the usage demand information to obtain a processing result; transmitting the processing result to the data requestor; and generating a verifiable claim (VC) for recording usage information of the original data.

Abstract: This specification provides a message transmission methods and apparatuses. One method includes: receiving a digital certificate sent by each blockchain node of a plurality of blockchain nodes in a blockchain relay communication network, wherein the digital certificate comprises identity information of each blockchain node of the plurality of blockchain nodes and a network identifier of a blockchain network of the blockchain relay communication network that comprises a corresponding blockchain node of the plurality of blockchain nodes; verifying that a digital signature of the digital certificate is authentic based on a public key of a certification authority (CA) issuing the digital certificate; and recording a mapping relationship between the identity information of each blockchain node of the plurality of blockchain nodes and the network identifier.

Abstract: The present specification provides a blockchain-based method and apparatus for managing a biological asset object. The method is performed by a node device in a blockchain network, a smart contract for managing a biological asset object is deployed in the blockchain, and processing logic corresponding to contract code of the smart contract includes biological asset object creation logic, the method including: obtaining an asset object creation transaction generated by a client, where the asset object creation transaction includes identification information of a target organism; and invoking the smart contract, executing the biological asset object creation logic, and creating a target biological asset object corresponding to the target organism based on the identification information of the target organism.

Abstract: The present specification discloses video matching. In a computer-implemented method, a plurality of feature vectors of a target video is obtained. A candidate video similar to the target video is retrieved from a video database based on the plurality of feature vectors of the target video. A time domain similarity matrix feature map is constructed between the target video and the candidate video based on the target video and the candidate video. Using the time domain similarity matrix feature map as an input into a deep learning detection model, a video segment matching the target video in the candidate video and a corresponding similarity is output.

Abstract: Computer-implemented methods, apparatuses, and systems are provided for synchronizing data based on a blockchain integrated station. The blockchain integrated station includes a central processing unit (CPU) and an intelligent network card. The intelligent network card includes a processor different from the CPU. The blockchain integrated station serves as a blockchain node of a blockchain network, and the intelligent network card is configured to inquiry other blockchain nodes of the blockchain network on whether there is to-be-synchronized block data; in response to determining that there is the to-be-synchronized block data, pull the to-be-synchronized block data from the other nodes of the blockchain network; and provide the to-be-synchronized block data to the CPU.

Abstract: A method for displaying relevant information of an electrical red packet includes: acquiring a browsing operation of a user on a target page; determining, in response to the browsing operation, at least one associated account of an account of the user; searching for relevant information of an electrical red packet that the at least one associated account participated in before a triggering time point of the browsing operation; and displaying the relevant information on the target page.

Abstract: Embodiments of the present specification disclose matching methods, apparatuses, and devices based on trusted asset data. One method comprises: analyzing trusted asset data of a first entity to obtain asset description information of the first entity, wherein the trusted asset data is stored on a trusted device; obtaining service information of a plurality of second entities; determining target service information that matches the first entity based on the asset description information and the service information; and sending the target service information to the first entity.

Abstract: A cryptographic acceleration card generates, using an attribute unique to a blockchain integrated station that comprises the cryptographic acceleration card, an identity private key for the blockchain integrated station. The cryptographic acceleration card generates a private key ciphertext by encrypting the identity private key. The cryptographic acceleration card stores the private key ciphertext.