Membership Inference Attack in Random Forests

Machine Learning (ML) offers many opportunities, but its reliance on personal data raises privacy concerns.One such example is the Membership Inference Attack (MIA), which aims to determine whether a specific data point was part of a model's training dataset.In this paper, we investigate this attack on Random Forests (RFs) and propose a method to quantify their vulnerability to MIA.We also demonstrate that in collaborative setups like federated learning, a client with access to the model and partial training dataset can establish MIA against other clients' training data.The effectiveness of our method is validated through experiments.