Prof. Hiroaki Kikuchi

Privacy Issues in Big Data and Privacy-preserving Data Mining

The utilization of big-data is expected to broadly contribute　to the innovative services and business models with accurate　model of our behaviors. Personal data is considered to be　highly values here and hence we have many issues in privacy.　This talk introduces a couple of incidents related to　personal data in big data business and clarify the risk of re-identification of individual from anonymized data. Current Personal Information Protection Act restricts the exchange of personal data among companies. There is no clear standard to specify procedure for reduction of risk of identification. To address the privacy issues in big data application, the notion of privacy-preserving data mining has been studied well. In this talk, the fundamental principle and some application of privacy-preserving data mining are introduced.

Biography of Hiroaki Kikuchi

He received B. E., M. E. and Ph.D. degrees from Meiji University in 1988, 1990 and 1994. After he working in Fujitsu Laboratories Ltd. in 1990, he had worked in Tokai university from 1994 through 2013. He is currently a professor in at Department of Frontier Media Science, School of Interdisciplinary Mathematical Sciences, Meiji University. He was a visiting researcher of the school of computer science, Carnegie Mellon University in 1997.

His main research interests are network security, cyrptographical protocl, privacy-preserving data mining, and fuzzy logic. He god the Best Paper Award for Young Researcher of Japan Society for Fuzzy Theory and Intelligent Informatics in 1990, the Best Paper Award for Young Researcher of IPSJ National Convention in 1993, the Best Paper Award of Symposium on Cryptography and Information Security in 1996, the IPSJ Research and Development Award Award in 2003, the Journal of Information Processing (JIP) Outstanding paper Award in 2010, and the IEEE AINA Best Paper Award in 2013.

He is a member of the Institute of Electronics, Information and Communication Engineers of Japan (IEICE), the Information Processing Society of Japan (IPSJ), the Japan Society for Fuzzy Theory and Systems (SOFT), IEEE and ACM. He in a director of IPSJ since 2013. He receives IPSJ Fellow.

Prof. Hanmin Jung

How to Help Researchers Using Prescriptive Analytics and Complex Event Processing?

Explosively increased Big Data and very fast technical evolutions require an entirely new analytics that is able to precisely analyze researchers’ activities until now and to provide research directions from now on. Prescriptive analytics shows fundamental difference with descriptive/predictive analytics in that it should provide multiple strategies to achieve a given research direction. Complex event processing also shows a new way to read implicit intentions from many kinds of activities such as publishing article, travelling on business, and attending conference. Thus, this talk shows a case study by explaining requirements and factors for implementing a personalized research service with InSciTe Advisory, as a data-intensive intelligent service, for helping to find plausible research directions. This talk also covers data gathering, information extraction from entities to simple events, reasoning, and Hadoop ecosystem.

Biography of Hanmin Jung

Hanmin Jung works as the head of the Dept. of Computer Intelligence Research and chief researcher at Korea Institute of Science and Technology Information (KISTI), Korea since 2004. He received his M.S. and Ph.D. degrees in Computer Science and Engineering from POSTECH, Korea in 1994 and 2003. Previously, he was a senior researcher at Electronics and Telecommunications Research Institute (ETRI), and worked as CTO at DiQuest Inc. Now, he is also professor of University of Science & Technology (UST), guest professor of Central Officials Training Institute, executive director of Korea Contents Association, director of several academic associations, and committee member of ISO/IEC JTC1/SC32.