Service prediction model training method and device for protecting data privacy

A technology for predicting models and protecting data, applied in the field of privacy protection

Active Publication Date: 2021-09-10
ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
View PDF6 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, data samples owned by different companies or institutions usually contain a large amou

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Service prediction model training method and device for protecting data privacy
  • Service prediction model training method and device for protecting data privacy
  • Service prediction model training method and device for protecting data privacy

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0082] The solutions provided in this specification will be described below in conjunction with the accompanying drawings.

[0083] Picture 1-1 It is a schematic diagram of an implementation structure of an embodiment disclosed in this specification. Wherein, the server communicates with multiple member devices respectively, and can perform data transmission. The number N of multiple member devices may be 2 or a natural number greater than 2. The communication connection can be through a local area network or through a public network. Each member device can have its own business data. Multiple member devices jointly train the business prediction model through data interaction with the server. The business prediction model trained in this way uses the business data of all member devices as data samples. The performance and robustness of the trained model will also be better.

[0084] The client-server architecture composed of the above server and more than two member device...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The embodiment of the invention provides a service prediction model training method and device for protecting data privacy. In the training process, member devices utilize object feature data held by the member devices to carry out prediction through the service prediction model, and update parameters used for updating model parameters are determined by utilizing prediction results, wherein the update parameters comprise a plurality of sub-parameters of a plurality of calculation layers for the service prediction model; the plurality of calculation layers are divided into a first type of calculation layers and a second type of calculation layers by using the plurality of sub-parameters, wherein the sub-parameter values of the first type of calculation layers are within a specified range; and privacy processing is performed on the sub-parameters of the first type of calculation layer, and the processed sub-parameters are output. The processed sub-parameters of the plurality of member devices may be aggregated into an aggregated sub-parameter. The member devices can obtain the aggregation sub-parameter of the first type of the calculation layers, and update the model parameters by using the aggregation sub-parameters and the sub-parameters of the second type of calculation layers.

Description

technical field [0001] One or more embodiments of this specification relate to the technical field of privacy protection, and in particular to a method and device for training a business prediction model that protects data privacy. Background technique [0002] With the development of artificial intelligence technology, neural networks have been gradually applied in areas such as risk assessment, speech recognition, face recognition and natural language processing. The neural network structure in different application scenarios has been relatively fixed. In order to achieve better model performance, more training data is needed. In fields such as medical care and finance, different companies or institutions have different data samples. Once these data are jointly trained, the accuracy of the model will be greatly improved. However, data samples owned by different companies or institutions usually contain a large amount of private data, once the information is leaked, it wil...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06N3/04G06N3/08G06Q10/04G06F21/62
CPCG06N3/084G06Q10/04G06F21/6245G06N3/045
Inventor 郑龙飞陈超超王力张本宇
Owner ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap