4 papers
DeFeed: Secure Decentralized Cross-Contract Data Feed in Web 3.0 for Connected Autonomous Vehicles
Xingchen Sun, Runhua Xu, Wei Ni +2
Smart contracts have been a topic of interest in blockchain research and are a key enabling technology for Connected Autonomous Vehicles (CAVs) in the era of Web 3.0. These contrac…
Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning
Runhua Xu, Shiqi Gao, Chao Li +2
Federated learning (FL) is inherently susceptible to privacy breaches and poisoning attacks. To tackle these challenges, researchers have separately devised secure aggregation mech…
TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning
Runhua Xu, Bo Li, Chao Li +3
Federated learning is a computing paradigm that enhances privacy by enabling multiple parties to collaboratively train a machine learning model without revealing personal data. How…
Blockchain Takeovers in Web 3.0: An Empirical Study on the TRON-Steem Incident
Chao Li, Runhua Xu, Balaji Palanisamy +4
A fundamental goal of Web 3.0 is to establish a decentralized network and application ecosystem, thereby enabling users to retain control over their data while promoting value exch…