Privacy Preserving
Privacy-preserving techniques aim to enable data analysis and machine learning while safeguarding sensitive information. Current research focuses on developing and improving methods like differential privacy, federated learning, homomorphic encryption, and data obfuscation, often applied to specific model architectures such as transformers and neural radiance fields. These advancements are crucial for addressing privacy concerns in various applications, including healthcare, finance, and AI-powered services, allowing for collaborative data analysis and model training without compromising individual privacy. The field is actively exploring the trade-offs between privacy guarantees, model accuracy, and computational efficiency.
Papers
A Learning-based Declarative Privacy-Preserving Framework for Federated Data Management
Hong Guan, Summer Gautier, Rajan Hari Ambrish, Yancheng Wang, Chaowei Xiao, Yingzhen Yang, Jia Zou
Privacy-Preserving Data Fusion for Traffic State Estimation: A Vertical Federated Learning Approach
Qiqing Wang, Kaidi Yang