ImpMIA: Leveraging Implicit Bias for Membership Inference Attack under Realistic Scenarios
This addresses data privacy concerns by improving MIA under more practical conditions, though it is incremental as it builds on existing white-box and implicit bias theory.
The paper tackles the problem of membership inference attacks (MIA) in realistic scenarios where black-box methods fail due to unrealistic assumptions, and introduces ImpMIA, a white-box attack that exploits implicit bias in neural networks to achieve state-of-the-art performance.
Determining which data samples were used to train a model-known as Membership Inference Attack (MIA)-is a well-studied and important problem with implications for data privacy. Black-box methods presume access only to the model's outputs and often rely on training auxiliary reference models. While they have shown strong empirical performance, they rely on assumptions that rarely hold in real-world settings: (i) the attacker knows the training hyperparameters; (ii) all available non-training samples come from the same distribution as the training data; and (iii) the fraction of training data in the evaluation set is known. In this paper, we demonstrate that removing these assumptions leads to a significant drop in the performance of black-box attacks. We introduce ImpMIA, a Membership Inference Attack that exploits the Implicit Bias of neural networks, hence removes the need to rely on any reference models and their assumptions. ImpMIA is a white-box attack -- a setting which assumes access to model weights and is becoming increasingly realistic given that many models are publicly available (e.g., via Hugging Face). Building on maximum-margin implicit bias theory, ImpMIA uses the Karush-Kuhn-Tucker (KKT) optimality conditions to identify training samples. This is done by finding the samples whose gradients most strongly reconstruct the trained model's parameters. As a result, ImpMIA achieves state-of-the-art performance compared to both black and white box attacks in realistic settings where only the model weights and a superset of the training data are available.