Membership Inference Attack in Random Forests - Akbarian, Fatemeh; Aminifar, Amir ; 0000-0002-1673-4733 (ESANN 2025, Bruges, Belgium, 2025-04-23 - 2025-04-25, 2025)
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.