Time：2:00-3:00 PM , Sep 15th 2023

Location：106,WICT

Abstract:

Distributed database system (DDBS) technology has shown its advantages with respect to query processing efficiency, scalability, and reliability. Moreover, by partitioning attributes of sensitive associations into different fragments, DDBSs can be used to protect data privacy. However, it is complex to design a DDBS when one has to optimize privacy and utility in a time-varying environment.

This talk will introduce a distributed prediction-randomness framework for the evolutionary dynamic multiobjective partitioning optimization of databases. In the proposed framework, two sub-populations contain individuals representing database partitioning solutions. One sub-population utilizes a Markov chain-based predictor to predict discrete-domain solutions for database partitioning when the environment changes, and the other sub-population utilizes the random initialization operator to maintain population diversity. In addition, a knee-driven migration operator is utilized to exchange information between two sub-populations. Experimental results show that the proposed algorithm outperforms the competing solutions with respect to accuracy, convergence speed, and scalability.

Bio:

Hua Wang is now a full time professor in the Institute for Sustainable Industries and Liveable Cities (ISILC) at Victoria University. Hua carries out research on machine learning and data analysis, with a particular focus on data mining, artificial intelligence, deep learning, big data, privacy preserving, access control and cyber security. As a Chief Investigator, Hua has successfully received seven large Australian Research Council (ARC) grants including four discovery grants and three linkage grants with AU$4.0M since 2006. He is also a successor for international grants such as two grants from Japan Society for the Promotion of Science (JSPS), one German–Australian grant (DAAD), one Norwegian government grant, three grants from Hong Kong and two from China.

Hua has published 400 refereed scholar papers with 10000+ citations, including 81 Q1 (ERA2018 ranked) Journal papers in machine learning, cyber security, Artificial Intelligence, privacy preserving and data analysis. Representative publications are on:
• ACM Transactions on Internet Technology
• ACM Transactions on Information Systems
• ACM Transactions on Knowledge Discovery from Data
• IEEE Transactions on Dependable and Secure Computing
• IEEE Transactions on Knowledge and Data Engineering
• IEEE Transactions on Automation Science and Engineering
• IEEE Transactions on Evolutionary Computation
• IEEE Transactions on Services Computing
• Proceedings of ACL, AAAI, CIKM, ICDE, ICDM and PAKDD.

Hua's h-index is 59 based on Google Scholar website. As a principal supervisor, he currently has 14 PhD students at VU.

Hua has been appointed as a College of Expert by the ARC since 2021, and has already been an ARC reviewer since 2009. He reviews various proposals each year for ARC DP, LP, DECRA, LIEF. Hua is Editor-in-Chief, Featured Journals, European Alliance for Innovation (EAI) Transactions on Scalable Information Systems, and also editor for PLOS ONE and World Wide Web Journal, Springer. He is involved in many prestigious international conferences such as PC members (AAAI2019, IJCAR2019) and chairs for the International Conference on Web Information Systems Engineering (WISE) 2015-2018.

Hua is the discipline leader of technology in engineering and science at VU, and a director of Oceania Cyber Security Centre (OCSC) consisting of eight Victorian universities with substantial support from the Victorian government, with engagement of industry. He is working as a committee member for:
• IEEE Blockchain Standards group
• IEEE COMSOC Project 1912 Privacy and Security Architecture for Consumer
Wireless Devices Working Group
• Japan Society for the Promotion of Science Alumni Association in Australia
(JSPSAAA)
• Data61 University Planning Committee.