Colloquia / Seminars / Industry Talks

The success of data mining relies on the availability of high quality data. To ensure quality data mining, effective information sharing between organizations becomes a vital requirement in today's society. Since data mining often involves person-specific and sensitive information like medical records, the public acquires a negative impression that data mining is a tool for privacy intrusion. Privacy-preserving data publishing is a study of eliminating privacy threats while, at the same time, preserving useful structures in the released data for data mining. This talk studies a collection of privacy threats in real life data publishing, and presents a unified solution to address these threats.

Benjamin Fung is an Assistant Professor of the Concordia Institute for Information Systems Engineering (CIISE) at Concordia University, Canada. He received the Ph.D. degree in Computing Science from Simon Fraser University, Canada. In the past, he worked at Business Objects Inc. (SAP) and designed reporting systems for various Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems. His research objective is to develop novel data analysis methods for real-life applications. His current research interests include data mining, database, privacy preservation, information security, and digital forensics, as well as their implications and applications in emerging technologies. His research has been supported in part by the Discovery Grants from Natural Sciences and Engineering Research Council of Canada (NSERC). http://www.ciise.concordia.ca/~fung/