Toward Selective Membership Inference Attack against Deep Learning Model

In this paper, we propose a selective membership infer-ence attack method that determines whether certain data corresponding to a specific class are being used as training data for a machine learning model or not. By using the proposed method, membership or non-membership can be inferred by generating a decision model from the prediction of the infer-ence models and training the confidence values for the data corresponding to the selected class. We used MNIST as an experimental dataset and Ten-sorflow as a machine learning library. Experimental results show that the proposed method has a 92.4% success rate with 5 inference models for data corresponding to a specific class.