Advanced New Technologies Co., Ltd.

Alibaba Group Holding Limited

Alipay (Hangzhou) Information Technology Co., Ltd.

Weiqiang Wang

Hong Jin

Lujia Chen

Wenbiao Zhao

Yuan Qi

Licui Gao

Qingyue Zhou

Wenhu Huo

Chengwei Huang

Xiaoxi Ji

Lingjuan Lv

Yuzhou Tang

The specification provides a method and a system for collaboratively updating a model by multiple parties for implementing privacy protection. A server can deliver an aggregation result of a t-th round of common samples to each participant i. Each participant i performs first update on a local ith model according to the t-th round of common samples and the aggregation result. Each participant i performs second update on the ith model obtained after the first update based on a first private sample fixed in a local sample set and a sample label thereof. Each participant i inputs a (t+1)th round of common samples that are used for a next round of iteration into the ith model obtained after the second update, and sends an output second prediction result to the server, so the server aggregates n second prediction results corresponding to n participants for a next round of iteration.

Methods, apparatuses, and systems for collaboratively updating model by multiple parties for implementing privacy protection

Methods and apparatus, including computer programs encoded on computer storage media, for monitoring resource transfer are provided. One of the methods includes: by a server, receiving a resource deposit request from a resource deposit initiator; performing, using a first risk identification model, a first risk identification on the target account according to the resource deposit request to obtain a first risk identification result; receiving a resource withdrawal request from a resource withdrawal initiator, and the resource withdrawal request requesting a resource withdrawal from the target account to the recipient account; performing, using a second risk identification model, a second risk identification on the target account according to the resource withdrawal request to obtain a second risk identification result; and determining, using a third risk identification model, a resource transfer risk monitoring result of the target account according to the first risk identification result and the second risk identification result.

Resource transferring monitoring method and device

A feature-selection system obtains a training data set and associated features; divides the training data set into a first number of training data subsets; and forms a plurality of feature-selecting data sets. A feature-selecting data set comprises a second number of training data subsets. The system processes, in parallel, each feature-selecting data set, which comprises: computing a first evaluation index for the features based on the feature-selecting data set; obtaining a group of index ranks corresponding to the features based on the first evaluation index; and obtaining a group of importance ranks corresponding to the features based on the feature-selecting data set and a machine-learning model. The system further obtains a group of total ranks by fusing groups of index ranks and groups importance ranks obtained from processing the plurality of feature-selecting data sets; and selecting target features from the features based on the group of total ranks.

Feature selection method, device and apparatus for constructing machine learning model

A method for data risk control comprises categorizing batch transaction data in a preset time period according to a first attribute, generating a graph indicator of a corresponding graph for each category of the batch transaction data according to a second attribute, the corresponding graph configured to identify risks in the batch transaction data, inputting the graph indicators corresponding to different categories of the batch transaction data into corresponding risk identification models, and determining whether the batch transaction data corresponding to the input graph indicators has a risk based on results output by the models. This scheme can quickly and accurately identify risks in batch transaction data.

Method and device for controlling data risk

A computer-implemented method includes: receiving, by a computing device, a text report request from a user device associated with a user; obtaining a behavior history and personal information of the user; inputting the behavior history and the personal information of the user into a model, to obtain a plurality of personalized evaluation results, each personalized evaluation result corresponding to a respective text report category of a plurality of text report categories, in which each personalized evaluation result indicates a predicted relevance of the corresponding text report category to a problem faced by the user, and in which the model includes a classification model trained using one or more supervised learning techniques on a plurality of user behavior history samples and a plurality of personal information samples; and determining an order in which the plurality of text report categories are to be presented to the user.

Sorting text report categories

Methods, systems, and computer-readable storage media for risk identification of service data using operations of determining, by a client-side computing device, a first service data corresponding to a first operation behavior associated with a user input on the client-side computing device, determining, by the client-side computing device, a first variable corresponding to the first service data, the first variable including a first eigenvalue, retrieving, by the client-side computing device, a second eigenvalue corresponding to a second operation behavior that was performed at a second time before the first operation behavior, generating, by the client-side computing device, a decay value by processing the first time and the second time a decay function, generating, by the client-side computing device, an aggregated data by processing the first variable, the second eigenvalue, and the decay value using an aggregation function, and determining, by the one or more processors, a risk associated with the first operation by processing the aggregated data using a risk identification model.

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Hangzhou, China

Weiqiang Wang