  With the development of information science and technology, various
industries have generated massive amounts of data, and machine learning is
widely used in the analysis of big data. However, if the privacy of machine
learning applications' customers cannot be guaranteed, it will cause security
threats and losses to users' personal privacy information and service
providers. Therefore, the issue of privacy protection of machine learning has
received wide attention. For centralized machine learning models, we evaluate
the impact of different neural network layers, gradient, gradient norm, and
fine-tuned models on member inference attack performance with prior knowledge;
For the federated learning model, we discuss the location of the attacker in
the target model and its attack mode. The results show that the centralized
machine learning model shows more serious member information leakage in all
aspects, and the accuracy of the attacker in the central parameter server is
significantly higher than the local Inference attacks as participants.
