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Rapid model forgetting method and system based on generative adversarial network

A model and fast technology, applied in the field of deep learning, can solve the problem of forgetting data based on machine learning, and achieve the effect of speeding up, obvious effect, and saving storage space

Pending Publication Date: 2022-06-03
GUANGZHOU UNIVERSITY
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  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The embodiment of the present application provides a fast model forgetting method and system based on generative confrontation network, aiming to solve the problem of data forgetting based on machine learning that cannot be satisfied by the existing technology

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  • Rapid model forgetting method and system based on generative adversarial network
  • Rapid model forgetting method and system based on generative adversarial network
  • Rapid model forgetting method and system based on generative adversarial network

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Embodiment Construction

[0028] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. It should be noted here that the description of these embodiments is used to help the understanding of the present invention, but does not constitute a limitation of the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0029] see figure 1 A flowchart of a method for fast model forgetting based on generative adversarial network shown in the embodiment, including:

[0030] S101. Take a third-party data with the same distribution as the data to be forgotten, input it into the original model, and perform sorting processing on the results output by the original model to obtain a first sorting result.

[0031] Suppose the data to be forgotten is D f , the trained original model is M i...

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Abstract

The invention discloses a rapid model forgetting method and system based on a generative adversarial network, and the method comprises the steps: inputting third-party data with the same distribution as to-be-forgotten data into an original model, sorting the output results of the original model, obtaining a first sorting result, initializing a generator into the original model, inputting the to-be-forgotten data into the generator, and obtaining a second sorting result; and sorting results output by the generator to obtain a second sorting result, alternately training the generator and the discriminator by using the first sorting result and the second sorting result, and stopping training until the discriminator cannot distinguish the distribution difference between the output of the to-be-forgotten data on the generator and the output of the third-party data on the original model. Member reasoning attacks are carried out on the generator, if an attack result is to-be-forgotten data and is not trained by the generator, forgetting succeeds, and the trained generator is used as a forgotten model; according to the method, the speed of forgetting the data in the model can be increased, and the effect is more obvious especially in a complex scene.

Description

technical field [0001] The invention relates to the technical field of deep learning, in particular to a method and system for fast model forgetting based on a generative confrontation network. Background technique [0002] In machine learning, especially in over-parameterized deep learning, the model trained on data will memorize a lot of information about the training data, which will bring serious privacy and security problems to users, such as attackers getting the model After the model is reversed, the data for training the model can be recovered from the trained model. Even if the attacker only queries the output of the model without knowing the internal details of the model, the member inference attack can also determine a certain data. whether to be used to train the model. The existence of these attacks shows that the model's memory of the training data will pose a serious threat to the user's privacy. Therefore, in order to protect the privacy of users, some laws...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N20/00
CPCG06N20/00G06F18/24G06F18/214
Inventor 陈孔阳黄耀王依文
Owner GUANGZHOU UNIVERSITY
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