Abstract: A method for predicting retrosynthesis of a compound molecule and a related apparatus. The method includes: obtaining a target molecule and determining the target molecule as a root node in a tree structure, then, expanding the first leaf node through a target retrosynthesis model to obtain a plurality of second leaf nodes, further, recursively processing the predicted molecule set corresponding to the second leaf nodes and determining a terminal node that satisfies a preset condition; and then, traversing path information corresponding to the terminal node to determine a retrosynthetic path of the target molecule. In this way, a retrosynthesis prediction process of a multi-step reaction is realized. Leaf nodes are gradually recursively expanded and screened, to ensure the reliability of reactants determined by the retrosynthesis prediction process of the multi-step reaction, thereby improving the accuracy of prediction of retrosynthesis of compound molecules.

Abstract: A plurality of variable data of personal attribute information associated with at least one vehicle insurance user is received at a prediction server. Based on a service scenario requirement, a pre-constructed prediction algorithm is selected. The plurality of variable data is processed by one or more processors using the pre-constructed prediction algorithm. At least one prediction result is generated as the prediction server.

Abstract: Techniques for data sharing between a data miner and a data provider are provided. A set of public parameters is downloaded from the data miner. The public parameters are data miner parameters associated with a feature set of training sample data. A set of private parameters in the data provider can be replaced with the set of public parameters. The private parameters are data provider parameters associated with the feature set of training sample data. The private parameters are updated to provide a set of update results. The private parameters are updated based on a model parameter update algorithm associated with the data provider. The update results is uploaded to the data miner.

Abstract: Techniques for data sharing between a data miner and a data provider are provided. A set of public parameters is downloaded from the data miner. The public parameters are data miner parameters associated with a feature set of training sample data. A set of private parameters in the data provider can be replaced with the set of public parameters. The private parameters are data provider parameters associated with the feature set of training sample data. The private parameters are updated to provide a set of update results. The private parameters are updated based on a model parameter update algorithm associated with the data provider. The update results is uploaded to the data miner.

Abstract: A privacy protection based training sample generation method includes: generating n d-dimensional transform vectors ? from original data to be mined, wherein the original data comprises m original samples, each original sample includes a d-dimensional original vector x and an output tag value y, m and d being natural numbers, and each transform vector ? is determined by a sum of yx of a plurality of original samples randomly selected from the m original samples; and determining the n transform vectors ? as training samples of a binary classification model.

Abstract: A privacy protection based training sample generation method includes: generating n d-dimensional transform vectors ? from original data to be mined, wherein the original data comprises m original samples, each original sample includes a d-dimensional original vector x and an output tag value y, m and d being natural numbers, and each transform vector ? is determined by a sum of yx of a plurality of original samples randomly selected from the m original samples; and determining the n transform vectors ? as training samples of a binary classification model.

Abstract: Techniques for data sharing between a data miner and a data provider are provided. A set of public parameters is downloaded from the data miner. The public parameters are data miner parameters associated with a feature set of training sample data. A set of private parameters in the data provider can be replaced with the set of public parameters. The private parameters are data provider parameters associated with the feature set of training sample data. The private parameters are updated to provide a set of update results. The private parameters are updated based on a model parameter update algorithm associated with the data provider. The update results is uploaded to the data miner.

Abstract: Techniques for data sharing between a data miner and a data provider are provided. A set of public parameters is downloaded from the data miner. The public parameters are data miner parameters associated with a feature set of training sample data. A set of private parameters in the data provider can be replaced with the set of public parameters. The private parameters are data provider parameters associated with the feature set of training sample data. The private parameters are updated to provide a set of update results. The private parameters are updated based on a model parameter update algorithm associated with the data provider. The update results is uploaded to the data miner.

Abstract: A plurality of variable data of personal attribute information associated with at least one vehicle insurance user is received at a prediction server. Based on a service scenario requirement, a pre-constructed prediction algorithm is selected. The plurality of variable data is processed by one or more processors using the pre-constructed prediction algorithm. At least one prediction result is generated as the prediction server.