Tag
This paper proposes a denoising-aware embedding inversion pipeline (DAEI) that effectively attacks noise-protected text embeddings, revealing significant privacy risks and challenging the efficacy of Gaussian noise as a defense.
This paper identifies a privacy vulnerability in RL-trained multimodal large reasoning models, which can leak sensitive facts in their reasoning traces even after unlearning, and proposes LEMUR, a training-free inference-time framework that uses entropy dynamics to detect and suppress such leakage.
This research paper investigates privacy leakage in tabular diffusion models, quantifying how training setups, synthesis choices, and attacker knowledge impact privacy risks. It reveals that adversaries can succeed without perfect knowledge or massive resources and highlights pitfalls in heuristic privacy metrics.