arXiv:2608.28934v1 Announce Type: new
Abstract: Differential privacy (DP) has traditionally been used to provide theoretical upper bounds on an algorithm's stability to changing its training data. In...
By Saloni Modi, Srivi Balaji, Yusong Zhu, Gautam Kamath, Kevin Tian
arXiv:2602. 02819v4 Announce Type: replace Abstract: Membership Inference Attacks (MIAs) aim to distinguish training points (members) from unseen data (non-members), and are widely used to quantify memorization and assess privacy risks.
By Mathieu Even, Cl\'ement Berenfeld, Linus Bleistein, Tudor Cebere, Julie Josse, Aur\'elien Bellet
The paper investigates whether inexpensive spectral metrics from the heavy‑tailed self‑regularisation framework can predict membership inference attack (MIA) vulnerability, offering a scalable alternative to costly shadow‑model attacks. Experiments on image and tabular classification tasks show that stable rank correlates positively with overall MIA success, while Log alpha‑Norm correlates negatively with MIA risk in low false‑positive regimes, outperforming conventional generalisation gap measures. These findings suggest that neural network spectra contain privacy leakage signals not captured by traditional overfitting metrics, pointing to spectral analysis as a promising direction for privacy auditing.
By Richard J. Preen, Jim Smith
arXiv:2607. 02903v1 Announce Type: cross Abstract: Explainability is central to building trustworthy AI, yet explanation interfaces can inadvertently provide adversaries with an expanded privacy-related attack surfaces.
By Varun Sharma, Kar Wai Fok, Vrizlynn L. L. Thing
Adaptive Diffusion Freezing (ADF) is a new privacy‑preserving framework for diffusion models that protects against membership inference attacks (MIAs). It uses cross‑timestep adaptive freezing training, where a mask matrix controls which data subsets participate at each diffusion timestep, reducing over‑memorization and aligning model behavior for member and non‑member samples. A pretraining‑based risk‑aware freezing policy estimates MIA risk and suppresses high‑risk subset‑timestep pairs, achieving a superior privacy‑utility‑efficiency trade‑off across multiple datasets.
By Jialu Guo, Xiao Han, Junjie Wu
arXiv:2605. 27292v2 Announce Type: replace Abstract: Privacy auditing aims to empirically assess privacy leakage in machine learning models using membership inference attacks (MIAs), and to derive lower bounds on differential privacy (DP) parameters.
By Mathieu Dagr\'eou, Aur\'elien Bellet