Abstract: Implementations of this specification provide query optimization methods, apparatuses, and systems for secure multi-party databases. In an implementation, a method includes: receiving a current query associated with a plurality of target database of a multi-party database system, generating a plurality of execution plans for the current query, determining, for each execution plan, a respective cost computation formula of a plurality of cost computation values for computing an execution cost of jointly executing the execution plan by the plurality of target databases, receiving a secure computation result from each of a plurality of query engines corresponding to the plurality of target databases, and determining an optimal execution plan having a lowest cost value in the plurality of cost computation formulas based on the secure computation result.

Abstract: This specification provides example data query methods, apparatuses, and systems for a multi-party secure database. In an example computer-implemented method, a central node receives a data query request from a data requester, where the data query request relates to one or more pieces of target data stored at one or more data providers. The central node determines whether the data query request satisfies respective security requirements of the one or more pieces of target data. In response to determining that the data query request satisfies respective security requirements of the one or more pieces of target data, the central node processes the one or more pieces of target data to obtain result data; and sends the result data to the data requester. The central node can restrict query of the target data by the data requester based on the security requirement of the target data stored at the data provider.

Abstract: In an implementation, operation instructions indicating application data to be used for performing one or more operations sent by a first client device are received. The application data is determined based on the operation instructions. One or more second client devices associated with the application data are determined. Operation codes in a trusted execution environment (TEE) associated with the application data to be executed are determined. That the operation codes has been executed for K times based on an indicator is determined. The operation codes are executed in the TEE based on the application data to generate an operation log. An indicator indicating a number of times the operation codes are executed is updated. The operation log and the indicator are sent as an operation record to a database server, the first client device, and the one or more second client devices to be stored.

Abstract: In an implementation, operation instructions indicating application data to be used for performing one or more operations sent by a first client device are received. The application data is determined based on the operation instructions. One or more second client devices associated with the application data are determined. Operation codes in a trusted execution environment (TEE) associated with the application data to be executed are determined. That the operation codes has been executed for K times based on an indicator is determined. The operation codes are executed in the TEE based on the application data to generate an operation log. An indicator indicating a number of times the operation codes are executed is updated. The operation log and the indicator are sent as an operation record to a database server, the first client device, and the one or more second client devices to be stored.

Abstract: The disclosure provides an information processing method and device. In one embodiment, an information processing method comprises receiving a request for generating questions inputted by a user, the request for generating questions includes a to-be-learned knowledge point; acquiring, from a knowledge graph for questions, a node path including a target node indicating the to-be-learned knowledge point, the nodes in the knowledge graph for questions indicating question-answering steps of existing questions, knowledge points tested in the question-answering steps, and questioning styles corresponding to the question-answering steps; and generating questions required by the user according to question-answering steps, knowledge points tested in the question-answering steps, and questioning styles corresponding to the question-answering steps indicated by nodes on the node path. The present disclosure enables generation of new questions and facilitates the expansion of a question bank.

Abstract: The disclosure provides an information processing method and device. In one embodiment, an information processing method comprises receiving a request for generating questions inputted by a user, the request for generating questions includes a to-be-learned knowledge point; acquiring, from a knowledge graph for questions, a node path including a target node indicating the to-be-learned knowledge point, the nodes in the knowledge graph for questions indicating question-answering steps of existing questions, knowledge points tested in the question-answering steps, and questioning styles corresponding to the question-answering steps; and generating questions required by the user according to question-answering steps, knowledge points tested in the question-answering steps, and questioning styles corresponding to the question-answering steps indicated by nodes on the node path. The present disclosure enables generation of new questions and facilitates the expansion of a question bank.

Abstract: A method includes acquiring user data of candidate users; mining a social business characteristic user in some of the candidate users according to the first social attribute data; training a classifier by using second social attribute data and second business object attribute data of the social business characteristic user; and inputting first social attribute data and first business object attribute data of a neighboring user to the classifier, and outputting a result of whether the neighboring user, in a period of time after the first period of time, is a social business characteristic user, wherein the neighboring user is a candidate user other than the social business characteristic user. The present disclosure increases the volume of associated data, and improves the accuracy of the classifier, thus improving the accuracy of identification, so that potential social business characteristic users in the first period of time can be identified.