Prof. (Dr.)  Yuh-Jong Hu (胡毓忠博士)

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 trend of big data analytics research areas through secured machine learning (ML)-as-a-Service to provide three types of analytics services, e.g.,  correlation, 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 public 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 could leverage their software agents from SaaS perspectives to provide services in an automated machine learning pipeline process. The greatest research challenge is how to integrate these SaaSs, 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 wihtout violating secuirty and privacy prinicples  in multi-tenant public cloud.

We are establishing types of SaaSs on multi-tenant public cloud computing platforms, such as  AWS and GCP. We assume machine learning models and datasets are all confidential so they should be well-protected while proceeding secured machine learning model’s offline training and online testing phases of data analytics services. We consider using  machine learning algorithms and integrating them with privacy and secured data protection algorithms, such as fully (or somewhat) homomorphic encryption (FHE), differential privacy, secure multi-party computation techniques,  to enable  a two-phase machine learning process. Furthermore, we are exploiting on how to effectively empower  types of SaaSs into automated machine learning (AutoML) pipeline to achieve secured machine learning for correlation, prediction, and even causal impact analytics in the multi-tenant public cloud environment. 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 was bestowed as the title of Honorary Professor at Amity Institute of Telecom Engineering & Management on 11th August, 2017 at Amity University, Noida, Uttar Pradesh, India.  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 multi-tenant public cloud computing environment. More specific 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 of 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, research issues include security and privacy of secured machine learning on the outsourcing multi-tenant public cloud, big data analytics for intelligent security,   big data analytics for causal impact inference, etc.


Projects, Publications & Talks


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Last Updated: Aug.-21st-2017
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