Binghui Wang

  • Assistant Professor of Computer Science

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Education

Ph.D. in Computer Engineering

Iowa State University

Research Interests

Data-driven Security and Privacy; Trustworthy Machine Learning; Big Data; Machine Learning

Publications

1. Binghui Wang, Jinyuan Jia, and Neil Zhenqiang Gong. \Graph-based Security and Privacy Analytics via Collective Classication with Joint Weight Learning and Propagation". In ISOC Network and Distributed System Security Symposium (NDSS), 2019. Acceptance rate: 89/521= 17.1% Distinguished Paper Award Honorable Mention

2. Binghui Wang, Jinyuan Jia, Le Zhang, and Neil Zhenqiang Gong. \Structure-based Sybil Detection in Social Networks via Local Rule-based Propagation". IEEE Transactions on Network Science and Engineering (TNSE), 2019. Fast tracked from our INFOCOM'17 paper

3. Binghui Wang, Le Zhang, and Neil Zhenqiang Gong. \SybilBlind: Sybil Detection in Social Web Services without Requiring Manual Labels" In Research in Attacks, Intrusions and Defenses (RAID), 2018. Acceptance rate: 33/145=22.8%

4. Binghui Wang, Le Zhang, and Neil Zhenqiang Gong. \SybilSCAR: Sybil Detection in Online Social Networks via Local Rule based Propagation". In IEEE International Conference on Computer Communications (INFOCOM), 2017. Acceptance rate: 292/1395=20.9% One of the 10 papers selected for fast tracking

5. Binghui Wang, Neil Zhenqiang Gong, and Hao Fu. \GANG: Detecting Fraudulent Users in Online Social Networks via Guilt-by-Association on Directed Graphs". In IEEE International Conference on Data Mining series (ICDM), regular paper, 2017. Acceptance rate: 72/778=9.25% Our method has been deployed by JD

6. Jinyuan Jia, Binghui Wang, Le Zhang, and Neil Zhenqiang Gong. \AttriInfer: Inferring User Attributes in Online Social Networks Using Markov Random Fields".

Media Appearances

Illinois Tech Part of Computing Partnership That Was Awarded Grant from the National Science Foundation

Forbes

The multidisciplinary Institute for Data, Econometrics, Algorithms, and Learning, or IDEAL, conducts advanced research and training in the field of data science. It aims to broaden research and education participation by underrepresented groups and will include high school and undergraduate students, as well as teachers (partnering with Math Circles of Chicago) and the public (through lectures in partnership with the Museum of Science and Industry).