Multi-party collaborative business prediction model updating method for realizing privacy protection

A predictive model and privacy protection technology, applied in the field of information security, can solve problems such as difficulty in meeting privacy protection requirements, single collaborative modeling method, etc.

Active Publication Date: 2020-06-23
ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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  • Claims
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Problems solved by technology

[0003] However, the existing collaborative modeling methods are relatively single, and it

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  • Multi-party collaborative business prediction model updating method for realizing privacy protection
  • Multi-party collaborative business prediction model updating method for realizing privacy protection
  • Multi-party collaborative business prediction model updating method for realizing privacy protection

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[0050] The following describes the solutions provided in this specification with reference to the drawings.

[0051] As mentioned earlier, multiple data parties need to ensure the privacy of their own data while co-training machine learning models (or predictive models) to prevent leakage of private data. In the proposed collaborative training method, a neutral global server (hereinafter referred to as the server) is set up, and a communication connection is established between each data party and the server, so that the data of all parties can use the server to achieve value integration. In this way, There is no need for direct communication between various data parties, thereby ensuring the privacy and security of the data of all parties to a certain extent.

[0052] specifically, figure 1 Shows a communication architecture diagram of a collaborative training machine learning model according to an embodiment, such as figure 1 As shown, multiple data parties (N are shown in the fi...

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Abstract

The embodiment of the invention provides a multi-party collaborative business prediction model updating method for realizing privacy protection. The multiple parties comprise a server and a pluralityof data parties; the server deploys a service prediction model for a service object. The method comprises the steps that any round of iterative updating is conducted on a service prediction model, andspecifically comprises the steps that a first data party participating in the round of iterative updating obtains a first current parameter part from a server, and the first current parameter part isobtained by conducting random selection based on current model parameters of the service prediction model; the first data party updates the first current parameter part by using local training data,and obtains a first updated parameter part based on at least one part of the updated parameter; the first data party partially uploads the first updating parameter to a server; and the server updatesthe service prediction model according to the first updating parameter part and other updating parameter parts received from other data parties participating in the current round of iterative updating.

Description

technical field [0001] One or more embodiments of this specification relate to the field of information security technology, and in particular, to a method and device for multi-party cooperative update of a service prediction model that realizes privacy protection. Background technique [0002] At present, the collaborative training of machine learning models by multiple data parties has sparked a research boom. For example, a large number of Internet of Things (IOT) devices hope to break through data silos, and by linking each IOT device together, they can learn and obtain corresponding machine learning models. The difficulty lies in how to ensure that the data privacy of each data party is not prying while collaborative modeling is used to maximize the value of data connection. [0003] However, the existing collaborative modeling methods are relatively single, and it is difficult to meet the higher privacy protection requirements in practical applications. Therefore, th...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06
CPCG06Q10/04G06Q10/067
Inventor 李龙飞周俊
Owner ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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