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
Private Algorithms for Stochastic Saddle Points and Variational Inequalities: Beyond Euclidean Geometry
Raef Bassily, Cristóbal Guzmán, Michael Menart
Differential Privacy Overview and Fundamental Techniques
Ferdinando Fioretto, Pascal Van Hentenryck, Juba Ziani
Differentially Private Continual Learning using Pre-Trained Models
Marlon Tobaben, Marcus Klasson, Rui Li, Arno Solin, Antti Honkela
Towards Robust Federated Analytics via Differentially Private Measurements of Statistical Heterogeneity
Mary Scott, Graham Cormode, Carsten Maple
FedDP: Privacy-preserving method based on federated learning for histopathology image segmentation
Liangrui Pan, Mao Huang, Lian Wang, Pinle Qin, Shaoliang Peng
Enhancing DP-SGD through Non-monotonous Adaptive Scaling Gradient Weight
Tao Huang, Qingyu Huang, Xin Shi, Jiayang Meng, Guolong Zheng, Xu Yang, Xun Yi
Gradient-Guided Conditional Diffusion Models for Private Image Reconstruction: Analyzing Adversarial Impacts of Differential Privacy and Denoising
Tao Huang, Jiayang Meng, Hong Chen, Guolong Zheng, Xu Yang, Xun Yi, Hua Wang