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Zheng Yan
Department of Communications and Networking

Zheng Yan

Visiting Professor, Academy Research Fellow, Docent

Contact information

Postal address
• Office B260, T building: Konemiehentie 2,
Work phone number
+358 50 4356035

Description

My research interests are in Information and Network Security, Trust and Privacy.

Department

Department of Communications and Networking

Honors and awards

Award or honor granted for a specific work
Department of Communications and Networking
Jan 2013

Outstanding leadeship award in IEEE TrustCom 2013 IEEE, Australia

Award or honor granted for a specific work
Department of Communications and Networking
Jan 2015

IEEE Distinguished Leadership Award at IEEE TrustCom/BigDataSE/ISPA-2015 Yan, Zheng, Finland

Award or honor granted for a specific work
Department of Communications and Networking
Jan 2012

Outstanding Service Award IEEE TrustID 2012, England

Award or honor granted for a specific work
Department of Communications and Networking
Jan 2014

The Best Faculty in Academic Year 2013 - 2014 Xidian University, China

Award or honor granted for a specific work
Department of Communications and Networking
Jan 2012

Outstanding leadership Award IEEE iThings 2012, France

Award or honor granted for a specific work
Department of Communications and Networking
Jan 2014

IEEE Outstanding Leadership Award at IEEE CIT 2014 IEEE CIT2014 Steering Committee, USA

Award or honor granted for a specific work
Department of Communications and Networking
Jan 2012

Outstanding Service Award IEEE TrustCom 2012, England

Award or honor granted for a specific work
Department of Communications and Networking
Jan 2014

Excellent Individual from Abroad in Shaanxi Province 2014 Shaanxi Province foreigner affair office, China

Award or honor granted for a specific work
Department of Communications and Networking
Jan 2014

IEEE Outstanding Service Award for IEEE DTWSM 2014 Organization IEEE CIT2014 Organization Committee, China

Award or honor granted for a specific work
Department of Communications and Networking
Jan 2014

Outstanding Leadership Award at IEEE HPCC/ICESS/CSS 2014 IEEE HPCC/ICESS/CSS2014 Organization Committee, France

Publications

Department of Communications and Networking

Identifying suspicious groups of affiliated-transaction-based tax evasion in big data

Publishing year: 2019 Information Sciences
Department of Communications and Networking

Adaptive security-related data collection with context awareness

Publishing year: 2019 Journal of Network and Computer Applications
Department of Communications and Networking

Predict Pairwise Trust based on Machine Learning in Online Social Networks

Publishing year: 2018 IEEE Access
Department of Communications and Networking

Message from the Program Chairs

Publishing year: 2018 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)
Department of Communications and Networking

IRTED-TL An Inter-Region Tax Evasion Detection Method Based on Transfer Learning

Publishing year: 2018 Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018
Department of Communications and Networking

A survey on network data collection

Publishing year: 2018 Journal of Network and Computer Applications
Department of Communications and Networking

Security Data Collection and Data Analytics in the Internet

Publishing year: 2018 IEEE Communications Surveys and Tutorials
Department of Communications and Networking

Privacy-preserving authentication and key agreement protocols for D2D group communications

Publishing year: 2018 IEEE Transactions on Industrial Informatics
Department of Communications and Networking

Guest Editorial Special Issue on Trust, Security, and Privacy in Crowdsourcing

Publishing year: 2018 IEEE Internet of Things Journal
Department of Communications and Networking

An Efficient Deep Learning Model to Predict Cloud Workload for Industry Informatics

Publishing year: 2018 IEEE Transactions on Industrial Informatics