Differential Privacy
Differential privacy (DP) is a rigorous framework for ensuring data privacy in machine learning by adding carefully calibrated noise to model training processes. Current research focuses on improving the accuracy of DP models, particularly for large-scale training, through techniques like adaptive noise allocation, Kalman filtering for noise reduction, and novel gradient processing methods. This active area of research is crucial for enabling the responsible use of sensitive data in various applications, ranging from healthcare and finance to natural language processing and smart grids, while maintaining strong privacy guarantees.
Papers
No-regret Exploration in Shuffle Private Reinforcement Learning
Shaojie Bai, Mohammad Sadegh Talebi, Chengcheng Zhao, Peng Cheng, Jiming Chen
Preempting Text Sanitization Utility in Resource-Constrained Privacy-Preserving LLM Interactions
Robin Carpentier, Benjamin Zi Hao Zhao, Hassan Jameel Asghar, Dali Kaafar
A Stochastic Optimization Framework for Private and Fair Learning From Decentralized Data
Devansh Gupta, A.S. Poornash, Andrew Lowy, Meisam Razaviyayn
Federated Low-Rank Adaptation with Differential Privacy over Wireless Networks
Tianqu Kang, Zixin Wang, Hengtao He, Jun Zhang, Shenghui Song, Khaled B. Letaief