activity
20182023
most citedLotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets

72 citations · 405 across the 33 of their papers we have counts for

collaborators
Showing 2018Show all

6 papers · 1 filter

cs.CR2018★ 1 cited

Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation

Binghui Wang, Jinyuan Jia, Neil Zhenqiang Gong

Many security and privacy problems can be modeled as a graph classification problem, where nodes in the graph are classified by collective classification simultaneously. State-of-t…

cs.CR2018★ 8 cited

SybilBlind: Detecting Fake Users in Online Social Networks without Manual Labels

Binghui Wang, Le Zhang, Neil Zhenqiang Gong

Detecting fake users (also called Sybils) in online social networks is a basic security research problem. State-of-the-art approaches rely on a large amount of manually labeled use…

cs.CR2018★ 12 cited

SybilFuse: Combining Local Attributes with Global Structure to Perform Robust Sybil Detection

Peng Gao, Binghui Wang, Neil Zhenqiang Gong +3

Sybil attacks are becoming increasingly widespread and pose a significant threat to online social systems; a single adversary can inject multiple colluding identities in the system…

cs.CR2018★ 7 cited

Structure-based Sybil Detection in Social Networks via Local Rule-based Propagation

Binghui Wang, Jinyuan Jia, Le Zhang +1

Sybil detection in social networks is a basic security research problem. Structure-based methods have been shown to be promising at detecting Sybils. Existing structure-based metho…

cs.CR2018★ 52 cited

Stealing Hyperparameters in Machine Learning

Binghui Wang, Neil Zhenqiang Gong

Hyperparameters are critical in machine learning, as different hyperparameters often result in models with significantly different performance. Hyperparameters may be deemed confid…

cs.CV2018

Robust Multi-subspace Analysis Using Novel Column L0-norm Constrained Matrix Factorization

Binghui Wang, Chuang Lin

We study the underlying structure of data (approximately) generated from a union of independent subspaces. Traditional methods learn only one subspace, failing to discover the mult…