2 citations · 2 across the 11 of their papers we have counts for
4 papers · 1 filter
On the Vulnerability of FHE Computation to Silent Data Corruption
Jianan Mu, Ge Yu, Zhaoxuan Kan +6
Fully Homomorphic Encryption (FHE) is rapidly emerging as a promising foundation for privacy-preserving cloud services, enabling computation directly on encrypted data. As FHE impl…
SPFL: A Self-purified Federated Learning Method Against Poisoning Attacks
Zizhen Liu, Weiyang He, Chip-Hong Chang +3
While Federated learning (FL) is attractive for pulling privacy-preserving distributed training data, the credibility of participating clients and non-inspectable data pose new sec…
DHSA: Efficient Doubly Homomorphic Secure Aggregation for Cross-silo Federated Learning
Zizhen Liu, Si Chen, Jing Ye +3
Secure aggregation is widely used in horizontal Federated Learning (FL), to prevent leakage of training data when model updates from data owners are aggregated. Secure aggregation…
SASH: Efficient Secure Aggregation Based on SHPRG For Federated Learning
Zizhen Liu, Si Chen, Jing Ye +3
To prevent private training data leakage in Fed?erated Learning systems, we propose a novel se?cure aggregation scheme based on seed homomor?phic pseudo-random generator (SHPRG), n…