8 papers
Data Overvaluation Attack and Truthful Data Valuation in Federated Learning
Shuyuan Zheng, Sudong Cai, Chuan Xiao +4
In collaborative machine learning (CML), data valuation, i.e., evaluating the contribution of each client's data to the machine learning model, has become a critical task for incen…
Secure Shapley Value for Cross-Silo Federated Learning (Technical Report)
Shuyuan Zheng, Yang Cao, Masatoshi Yoshikawa
The Shapley value (SV) is a fair and principled metric for contribution evaluation in cross-silo federated learning (cross-silo FL), wherein organizations, i.e., clients, collabora…
PGB: Benchmarking Differentially Private Synthetic Graph Generation Algorithms
Shang Liu, Hao Du, Yang Cao +3
Differentially private graph analysis is a powerful tool for deriving insights from diverse graph data while protecting individual information. Designing private analytic algorithm…
Extracting Spatiotemporal Data from Gradients with Large Language Models
Lele Zheng, Yang Cao, Renhe Jiang +4
Recent works show that sensitive user data can be reconstructed from gradient updates, breaking the key privacy promise of federated learning. While success was demonstrated primar…
HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization
Shun Takagi, Li Xiong, Fumiyuki Kato +2
Human mobility data offers valuable insights for many applications such as urban planning and pandemic response, but its use also raises privacy concerns. In this paper, we introdu…
Enhancing Privacy of Spatiotemporal Federated Learning against Gradient Inversion Attacks
Lele Zheng, Yang Cao, Renhe Jiang +4
Spatiotemporal federated learning has recently raised intensive studies due to its ability to train valuable models with only shared gradients in various location-based services. O…