R. Wang
16 records found
1
Authored
FEVERLESS
Fast and Secure Vertical Federated Learning based on XGBoost for Decentralized Labels
Vertical Federated Learning (VFL) enables multiple clients to collaboratively train a global model over vertically partitioned data without leaking private local information. Tree-based models, like XGBoost and LightGBM, have been widely used in VFL to enhance the interpretati ...
Federated Learning (FL) is a beneficial decentralized learning approach for preserving the privacy of local datasets of distributed agents. However, the distributed property of FL and untrustworthy data introducing the vulnerability to backdoor attacks. In this attack scenario ...
PIVODL
Privacy-Preserving Vertical Federated Learning Over Distributed Labels
Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing their private local data. Nonparametric models like gradient boosting decision trees (GBDTs) have b ...
Your Smart Contracts Are Not Secure
Investigating Arbitrageurs and Oracle Manipulators in Ethereum
Smart contracts on Ethereum enable billions of dollars to be transacted in a decentralized, transparent and trustless environment. However, adversaries lie await in the Dark Forest, waiting to exploit any and all smart contract vulnerabilities in order to extract profits from ...