A Method of Information Protection for Collaborative Deep Learning under GAN Model Attack.

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Abstract

Deep learning has recently gained more and more popularity, because of its high accuracy and wide range of coverage. In particular, deep learning is widely used in the medical field. Because in the field of image classification and biological applications, the accuracy of deep learning is very high. Unfortunately, even under the collaborative deep learning, there is still serious risk of information leakage. Moreover, the risk of information leakage in the medical field is greater and the harm is even greater. For example, medical treatment data may be leaked to third-party organizations. When these important medical data is illegally used by for-profit organizations or obtained by criminals, it will not only lead to the disclosure of personal privacy information, but also cause serious economic losses to the victims. However, the victim cannot delete the leaked information by itself or limit the scope and use of the information that has been leaked. Therefore, the adverse effects are unimaginable. This paper mainly studies the information protection methods under GAN model attack, in order to find a better way to prevent attacks and effectively protect information.