Xuebin Ma

Graph Publishing with Local Differential Privacy for Hierarchical Social Networks

2020

With the spread of social network services, social media applications have obtained a significant amount of personal data and relational information from their users. In the era of big data, data should to be shared …

AdaDP-CFL: Cluster Federated Learning with Adaptive Clipping Threshold Differential Privacy

2024

Federated learning is a distributed machine learning approach that enables multiple clients to train models collaboratively. As its data remains stored locally on each client, this approach significantly enhances the protection of private information. However, …

PDP-FD: Federated Knowledge Distillation Based on Personalized Differential Privacy

2025

Federated learning (FL) is a privacy-preserving distributed machine learning approach that enables training by exchanging model parameters without uploading local private data. However, the data heterogeneity across clients poses significant challenges in achieving personalized privacy …