Machine learning (ML) explainability is central to algorithmic transparency in high-stakes settings such as predictive diagnostics and loan approval. Yet these same domains demand …
Privacy hardening for explanation methods against membership inference leakage.
In membership inference attacks (MIAs), an adversary observes the predictions of a model to determine whether a sample is part of the model's training data. Existing MIA defenses …
Differential Privacy (DP) has emerged as a rigorous formalism to reason about quantifiable privacy leakage. In machine learning (ML), DP has been employed to limit …
When multiple parties that deal with private data aim for a collaborative prediction task such as medical image classification, they are often constrained by data protection …