Non-third-party federal learning method and system based on secret sharing and homomorphic encryption

A homomorphic encryption and secret sharing technology, applied in the field of machine learning, can solve problems such as high communication complexity and large system resources, and achieve the effect of reducing consumption

Active Publication Date: 2021-10-19
深圳市洞见智慧科技有限公司
View PDF8 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] It can be seen that, limited by the complexity and performance bottleneck of the MPC protocol, the untrusted th

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
  • Non-third-party federal learning method and system based on secret sharing and homomorphic encryption
  • Non-third-party federal learning method and system based on secret sharing and homomorphic encryption
  • Non-third-party federal learning method and system based on secret sharing and homomorphic encryption

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the application with reference to the drawings in the embodiments of the application. Apparently, the described embodiments are only some of the embodiments of the application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art based on this application belong to the scope of protection of this application.

[0062] In order to solve the technical problems of the MPC-based non-trusted third-party federated learning scheme consuming more system resources and high communication complexity in the prior art, the embodiment of this application provides a non-authorized learning scheme based on secret sharing and homomorphic encryption. Tripartite federated learning method and system.

[0063] see figure 1 , figure 1 A schematic flowchart of a third-party federated learning method based on secret ...

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 third-party-free federated learning method and system based on secret sharing and homomorphic encryption, and the method comprises the steps: enabling each participant to share an intermediate result of model training through a secret, enabling the secret obtained by the participant to be interacted with other participants in a homomorphic encryption mode, and carrying out the model training, and finally, enabling an appointed result party to obtain a final result of model training, so that model training in federated learning without a trusted third-party mechanism is realized. Moreover, since only an intermediate result of model training is shared secretly, compared with a mode that original sample data and model parameters are split and secretly shared in an MPC-based untrusted third-party federated learning scheme, the consumption of system resources and the communication complexity are greatly reduced.

Description

technical field [0001] This application relates to the field of machine learning technology, in particular to a third-party-free federated learning method and system based on secret sharing and homomorphic encryption. Background technique [0002] Today, big data-driven artificial intelligence technology has been widely used in finance, retail, medical and other fields. In order to get a better model, it often requires the support of a large amount of data, but in reality, the data is often distributed in different institutions. [0003] Data in different fields often have great complementarity, and there is a great demand for data fusion among different organizations. However, based on factors such as privacy protection, self-interest, and policy regulation, it is difficult for organizations to aggregate data directly. [0004] In the traditional joint modeling method, the sample data needs to be classified into a specific environment of a certain party or a third party. ...

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): G06N20/20G06F21/60
CPCG06N20/20G06F21/602
Inventor 黄一珉王湾湾冯新宇何浩姚明
Owner 深圳市洞见智慧科技有限公司
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