Image generation method based on adaptive gradient cutting under differential privacy protection

Abstract

The invention discloses an image generation method based on adaptive gradient cutting under differential privacy protection, and solves the problems of excessive noise and unreasonable cutting in a gradient cutting method based on training data in a conventional privacy protection-based model generation algorithm. Meanwhile, a cutting threshold distribution method based on a model layer gradient cutting method is improved, the efficiency of network learning is improved on the premise that the cutting threshold is reasonably distributed, and the magnitude of gradient noise added after cutting is further reduced under the condition that finer cutting granularity is selected. Therefore, the method can provide a more reasonable gradient cutting mode and lower noise addition. Compared with the prior art, the method has the advantages that the generation model can be more quickly converged to a local optimal solution under the conditions of more appropriate cutting and smaller noise addition, and the purpose of generating a high-quality picture is achieved.

Description

technical field [0001] The present invention relates to the field of differential privacy and artificial intelligence. It uses differential privacy technology to ensure the privacy of training data in machine learning scenarios, and uses adaptive tailoring technology to ensure the availability of the adversarial generation network model after gradient noise. A privacy-preserving approach to high-quality image generation. Background technique [0002] Machine learning is an integral part of today's society. Every aspect of people's lives, from food delivery to short videos, to medical education, cannot do without the help of machine learning algorithms. Although machine learning facilitates human society, this convenience is not without cost. Machine learning models require a large amount of training data to ensure the availability and robustness of the model. However, the current means of attacking machine learning can already infer whether a certain data is in the trainin...

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Problems solved by technology

[0049] The invention discloses an image generation method based on adaptive gradient cropping under differential privacy protection, which solves the problem of excessive noise and unreasonable cropping in the previous generation model algorithm based on privacy protection under the gradient clipping method based on training data. At the same time, the distribution method of the clipping threshold under the gradient clipping method based on the model layer is improved, and the efficiency of network learning is accelerated under the premise of using a reasonable distribution of the clipping threshold, and the gradient added after clipping is further reduced when a finer clipping granularity is selected. the size of the noise

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