PEARL is a framework for human‑centric cyber‑physical systems that uses a dual‑path Early‑Exit Deep Q‑Network to control the trade‑off between privacy and utility. By training per‑branch binary labels—Utility Confidence Labels (UCL) and Privacy Confidence Labels (PCL)—based on mutual information between private states and observable actions, PEARL selects the shallowest exit that satisfies both privacy and utility constraints, avoiding noise injection. The system includes an MI‑based feedback loop to detect behavioral drift and trigger retraining, and experiments on a smart‑home HVAC system and a VR smart classroom show a 25.67% reduction in adversarial state‑inference accuracy with only a 10‑16% utility cost.
By Mojtaba Taherisadr, Salma Elmalaki
arXiv:2512. 22287v3 Announce Type: replace-cross Abstract: Synthetic appliance data are essential for developing non-intrusive load monitoring algorithms and enabling privacy preserving energy research, yet the scarcity of labeled datasets remains a significant barrier.
By Zikun Guo, Adeyinka. P. Adedigba, Rammohan Mallipeddi
arXiv:2606. 02563v1 Announce Type: new Abstract: Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\varepsilon_i$) according to institutional policies and data sensitivity.
By Farhin Farhad Riya, Olivera Kotevska, Jinyuan Stella Sun
Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\varepsilon_i$) according to institutional policies and data sensitivity. In practice, many HDP-FL systems employ $\varepsilon$-aware server aggregation to improve model utility by re-weighting client updates according to their declared privacy budgets.
arXiv:2409. 01062v4 Announce Type: replace Abstract: Model Inversion (MI) attacks pose a significant privacy threat by reconstructing private training data from machine learning models.
By Viet-Hung Tran, Ngoc-Bao Nguyen, Son T. Mai, Hans Vandierendonck, Ira Assent, Alex Kot, Ngai-Man Cheung
The paper introduces a general learning framework that protects privacy in federated learning by distorting model parameters, enabling a trade‑off between privacy and utility. The algorithm supports arbitrary privacy measurements and delivers personalized utility‑privacy balances for each parameter, client, and communication round. The authors prove that the gap between their algorithm’s utility loss and the optimal loss is sub‑linear in iterations, provide a convergence rate, and demonstrate empirically that their method outperforms baselines under the same privacy budget.
By Xiaojin Zhang, Wenjie Li, Yiming Li, Wei Chen, Shutao Xia, Qiang Yang