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The advent of the information age has led to the problems of informationoverload and unclear demands. As an information filtering system, personalizedrecommendation systems predict users' behavior and preference for items andimproves users' information acquisition efficiency. However, recommendationsystems usually use highly sensitive user data for training. In this paper, weuse the latent factor model as the recommender to get the list of recommendeditems, and we representing users from relevant items Compared with thetraditional member inference against machine learning classifiers. We constructa multilayer perceptron model with two hidden layers as the attack model tocomplete the member inference. Moreover, a shadow recommender is established toderive the labeled training data for the attack model. The attack model istrained on the dataset generated by the shadow recommender and tested on thedataset generated by the target recommender. The experimental data show thatthe AUC index of our attack model can reach 0.857 on the real datasetMovieLens, which shows that the attack model has good performance.

Membership Inference Attacks Against Latent Factor Model