4 papers
CoSIFL: Collaborative Secure and Incentivized Federated Learning with Differential Privacy
Zhanhong Xie, Meifan Zhang, Lihua Yin
Federated learning (FL) has emerged as a promising paradigm for collaborative model training while preserving data locality. However, it still faces challenges from malicious or co…
Private and Communication-Efficient Federated Learning based on Differentially Private Sketches
Meifan Zhang, Zhanhong Xie, Lihua Yin
Federated learning (FL) faces two primary challenges: the risk of privacy leakage due to parameter sharing and communication inefficiencies. To address these challenges, we propose…
Sketches-based join size estimation under local differential privacy
Meifan Zhang, Xin Liu, Lihua Yin
Join size estimation on sensitive data poses a risk of privacy leakage. Local differential privacy (LDP) is a solution to preserve privacy while collecting sensitive data, but it i…
Partition-based differentially private synthetic data generation
Meifan Zhang, Dihang Deng, Lihua Yin
Private synthetic data sharing is preferred as it keeps the distribution and nuances of original data compared to summary statistics. The state-of-the-art methods adopt a select-me…