Prof. (Dr.)  Yuh-Jong Hu(胡毓忠教授)

Emerging Network Technologies (ENT) Lab
Department of Computer Science
National Chengchi University
Wen-Shan District 11605, Taipei, Taiwan(R.O.C.)
jong at, Phone:+886-2-2938-7620

Big data analytics has becoming one of the most important research areas of computer science and related fields. In Emerging Network Technology (ENT) Lab., we are exploiting  new trends of big data analytics research areas through secured machine learning (ML)-as-a-Service to provide three types of analytics services, e.g.,  correlation description, prediction, and causal impact in the multi-tenant public cloud. Possible applications are intelligent security analytics, new policy evaluation, causal inference in economic and marketing, etc.

Public cloud platform provides multi-tenant software leased services. Therefore, various participants can use software agents to effectively conduct their own services in an automated machine learning pipeline (AutoML). We are exploiting on how to construct numerous software-as-a-service (SaaS) services on a Platform-as-a-Service (PaaS)framework delivered by well-known cloud service providers, such as Amazon AWS and Google GCP, to achieve secured big data analytics on correlation discovery, trend predication, and causal impact inference.

 Given a secured cloud provider, it is still considered as an honest-but-curious adversary. Similar assumption will also be applied to other participants, including data brokers, data users, machine learning model builders and service users. These participants will leverage their software agents from SaaS perspectives to provide automated machine learning pipeline services. The greatest research challenge is how to integrate three types of SaaS, i.e. security-as-a-service (Sec-as-a-Service), machine learning-as-a-service (ML-as-a-Service) and data broker-as-a-service (DB-as-a-Service), to achieve secured big data analytics in the public cloud.

 We are establishing those SaaS on public cloud computing environment in AWS and GCP. We assume a machine learning model and data are all confidential so they should be protected when we proceed a machine learning model’s training and online testing on data analytics. We consider using several machine learning algorithms and integrate them with privacy and secured data protection algorithms to enable a two-phase machine learning process. Furthermore, we are investigating on how to effectively embed three types of those SaaS into automated machine learning (AutoML) pipeline to achieve secured machine learning for big data analytics in the public cloud. For more detailed information, please refer to the head of ENT Lab., Prof. Yuh-Jong Hu (Phone: +886-2-2938-7620, Email: jong at


Dr. Yuh-Jong Hu is a full professor of the Department of Computer Science at the National Chengchi University (NCCU), Taiwan (R.O.C.). Dr. Hu’s education backgrounds are: B.S., Department of Applied Mathematics , National Chengchi University (1976-1980), M.S., Operations Research & System Analysis (ORSA), University of North Carolina at Chapel Hill (1982-1984), U.S.A., and Ph.D., Department of Computer Science, University of Missouri at Rolla (1987-1990), U.S.A. Dr. Hu is in charge of Emerging Network Technology (ENT) Lab. Before he was promoted to a full professor in 2002, he had done several important works on software agent's trust, authentication, authorization, and delegation through digital certificates management in the Agent-Oriented Public Key Infrastructure (APKI).

Dr. Hu had been interested in the Semantic Web technologies since 2002, including ontologies and rules for privacy-preserving data integration and access on the Web. He has been continuously providing services for several well-known Semantic and Social Web related conferences and journals as international program committee members and journal reviewers, including RuleML, WIMS, WISE, KES-IDT, WI/IAT, WEBIST, WI&C, WIAS, JISE, etc. Recently, he is interested in secure and privacy-preserving big data analytics in the open public cloud computing environment. More specifically research issues are: security and privacy for cloud computing, big data analytics for intelligent security, privacy-preserving big data prediction and causal impact analytics, anomalous detection for business intelligent process etc. Furthermore, he is also interested in the multidisciplinary research on security and privacy protection from technology and legal perspectives.

We are currently recruiting master and Ph.D. students for big data analytics of intelligent security and data protection research project, issues include security and privacy for secured machine learning on the outsourcing public cloud, big data analytics for intelligent security,  privacy-preserving big data analytics for causal impact inference, etc.


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Last Updated: Feb.-23rd-2017
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