The paper introduces a contact tracing algorithm that incorporates differential privacy to protect users’ risk scores from adversarial inference. By releasing each risk score with an epsilon of 1, the algorithm achieves a two- to ten-fold reduction in infection rates across two leading COVID‑19 agent‑based simulators. This work claims to be the first to provide differential privacy guarantees for risk score disclosure in COVID‑19 contact tracing.
By Rob Romijnders, Christos Louizos, Yuki M. Asano, Max Welling
arXiv:2608.20118v1 Announce Type: cross
Abstract: The detection of rare disease-associated cell subsets from high-dimensional single-cell measurements is critical for understanding diseases such as l...
By \c{S}. Selcan Magara, Esther Havemann, Debora Jutz, Ali Burak \"Unal, Mete Akg\"un
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.
By Hao Fang, Yixiang Qiu, Hongyao Yu, Wenbo Yu, Jiawei Kong, Baoli Chong, Bin Chen, Xuan Wang, Shu-Tao Xia, Ke Xu
arXiv:2608. 13773v1 Announce Type: cross Abstract: Neural networks are increasingly deployed in high-stakes applications with growing privacy leakage concerns.
By Leonardo Magliolo, Vito Paolo Pastore, Giuseppe Valenzise, Enzo Tartaglione
Efficient Provably Private Classification with a Tabular Foundation Model introduces PrivTab, a tabular foundation model that embeds differential privacy directly into its architecture. Pretrained on simulated datasets, PrivTab uses in-context learning to convert sensitive rows into compact, provably private summaries, enabling a single forward pass for classification. The model outperforms private linear and neural‑network baselines under moderate-to-strong privacy, shows negligible membership leakage, maintains well‑calibrated predictions, and reduces dataset fitting time by 10,000×.
By Talal Alrawajfeh, Cristiana Diaconu, Ossi R\"ais\"a, Sebastian Rodriguez Beltran, Yuan He, John Bronskill, Richard E. Turner, Antti Honkela
The paper demonstrates that publicly available clinical machine learning models, such as logistic regression (LR), pose significant privacy risks because attackers can recover model parameters and identify training cohorts through various membership inference attacks. The authors show that even small cohorts can be reliably identified and that common practices like cross-validation can worsen the risk. To mitigate this, they propose a quantum-inspired defense that tensorizes discretized models into tensor trains (TTs), which obfuscates parameters, preserves accuracy, and maintains interpretability while providing black‑box protection comparable to Differential Privacy.
By Jos\'e Ram\'on Pareja Monturiol, Juliette Sinnott, Roger G. Melko, Mohammad Kohandel