A Domain Adaptive Privacy Protection Method Based on Differential Privacy for Deep Neural Networks

Abstract

The invention discloses a deep neural network-oriented domain adaptation privacy protection method based on differential privacy. A novel deep network framework is proposed to preserve data privacy while implementing domain adaptation techniques. In real-world scenarios where transfer learning is performed, such as schools and hospitals, the training data set is usually private, and there is no flexible solution for protecting domain adaptability to technical privacy, so the present invention has strong practicability. The invention uses the idea of ​​adversarial learning to perform domain adaptation training, and for the first time protects the privacy of the domain adaptation training process through differential privacy. The experimental results show that this model can complete the domain adaptation task with ideal accuracy under suitable privacy consumption.

Description

technical field [0001] The invention relates to a deep neural network-oriented domain adaptation privacy protection method based on differential privacy, and belongs to the field of artificial intelligence security. Background technique [0002] Deep learning has shown great capabilities in solving many problems, such as speech recognition and computer vision. But in order to get good performance, deep learning requires a lot of data to train the model, which usually requires a lot of time and effort. The differences in datasets also make it difficult for a trained model to directly classify another dataset. These requirements have prompted the development of transfer learning, which can transfer a model trained on the source domain and use it to classify the target domain data. Domain adaptation techniques are one way to implement transfer learning. [0003] Specifically, domain adaptation techniques aim to map the source domain data and the target domain data to the sam...

AI Technical Summary

Problems solved by technology

But in order to achieve good performance, deep learning requires a lot of data to train the model, which usually requires a lot of time and effort

The difference in data sets also makes it difficult for a trained model to directly classify another data set

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