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Model training method and device for protecting privacy in distributed system

A distributed system, privacy protection technology, applied in the field of machine learning, to improve the efficiency of data transmission, protection from leakage, and good prediction performance

Active Publication Date: 2020-12-18
ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, in the scenario where multiple parties jointly train the model, data security and data privacy issues are a great challenge

Method used

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  • Model training method and device for protecting privacy in distributed system
  • Model training method and device for protecting privacy in distributed system
  • Model training method and device for protecting privacy in distributed system

Examples

Experimental program
Comparison scheme
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Embodiment approach

[0096]According to an embodiment, it is assumed that the first bit string includes the first bit value at the first position; the flip unit 43 is configured to:

[0097]If the first bit value is 1, keep its bit value unchanged with a third probability;

[0098]If the value of the first bit is 0, turn it to 1 with a fourth probability;

[0099]Wherein, at least one of the third probability and the fourth probability is determined according to the second privacy budget.

[0100]According to another implementation manner, for the first bit value of the above-mentioned first position, the inversion unit 43 is configured to:

[0101]If the first position is an even number, set its flip value to 1 with a fifth probability;

[0102]If the first position is an odd number, set its flip value to 1 with a sixth probability;

[0103]Wherein, the fifth probability and the sixth probability are different values ​​respectively determined according to the second privacy budget and the length of the first bit string.

[01...

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Abstract

The embodiment of the invention provides a model training method and device for protecting privacy in a distributed system, the distributed system at least comprises a plurality of data platforms, andthe method can be executed by any data platform and comprises the following steps: firstly, utilizing a local sample set to train a service prediction model to obtain a floating point numerical valueof each weight parameter; converting the floating point numerical value of each weight parameter into a binary bit value by using a first randomization algorithm to obtain a first bit string; then, randomly overturning the bit value of each position in the first bit string by using a second randomization algorithm to obtain a second bit string. Thus, a second bit string may be provided as a localized training result for each weight parameter of the traffic prediction model.

Description

Technical field[0001]One or more embodiments of this specification relate to the field of machine learning, and in particular to a model training method and device for protecting privacy in a distributed system.Background technique[0002]The rapid development of machine learning has enabled various machine learning models to be applied in a variety of business scenarios. Since the prediction performance of a model depends on the richness and availability of training samples, in order to obtain a business prediction model with better performance, it is often necessary to comprehensively use training data from multiple platforms to jointly train the model.[0003]Specifically, in a scenario where data is vertically distributed, multiple platforms may each have different characteristic data of the same batch of business objects. For example, in a business classification analysis scenario based on machine learning, the electronic payment platform owns the merchant's transaction flow data, ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F21/60G06N3/04G06N3/08
CPCG06F21/604G06N3/08G06N3/045G06N3/044
Inventor 熊涛
Owner ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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