arXiv Machine Learning
Timing is Everything: Inferring Temporal Membership in Continually Updated Models
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arXiv Computer Vision
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Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses
The paper "Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses" provides a comprehensive review of model inversion (MI) attacks that exploit trained deep neural networks to reconstruct private training data. It traces the evolution of MI from early machine‑learning contexts to recent DNN‑based attacks across various modalities and learning tasks, offering a detailed taxonomy and comparative analysis of both attacks and defenses. The authors also present an open‑source toolbox on GitHub to support further research in this area.
arXiv AI
Jun 24
Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training
arXiv:2507. 01752v4 Announce Type: replace-cross Abstract: Gradient-based optimization is the workhorse of deep learning, offering efficient and scalable training via backpropagation.
arXiv Machine Learning
Aug 18
Model Inversion Attacks: A Survey of Approaches and Countermeasures
arXiv:2411. 10023v3 Announce Type: replace Abstract: Deep neural networks have enabled numerous studies and applications on both Euclidean data, such as images and text, and non-Euclidean data, such as graphs.
arXiv Machine Learning
Jun 17
Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs
arXiv:2606. 17110v1 Announce Type: cross Abstract: Large Language Models are increasingly trained on proprietary or sensitive data, from private healthcare and financial records to user conversations containing secrets.
arXiv Machine Learning
Jul 15
Reducing information dependency does not cause training data privacy. Adversarially non-robust features do
arXiv:2607. 12354v1 Announce Type: new Abstract: In this paper, we challenge the prevailing view that information dependency (including rote memorization) drives training data exposure to image reconstruction attacks.
arXiv Machine Learning
Aug 14
Noise as a Probe: Membership Inference Attacks on Diffusion Models Leveraging Initial Noise
arXiv:2601. 21628v2 Announce Type: replace-cross Abstract: Diffusion models have achieved remarkable progress in image generation, but their increasing deployment raises serious concerns about privacy and copyright.