Title: Protecting Individuals' Privacy When Publishing High-Dimensional Data
Abstract: GPS-equipped devices, smart card automated fare collection systems, and sensory technology are but a few examples of advanced, yet affordable, data-generating technologies that are an integral part of modern society. To enhance user experience or provide better services, service providers rely on collecting person-specific information from users. Thus, the collected data is studied and analyzed in order to extract useful information. It is a common practice for the collected data to be shared with a third-party for data analysis. However, the shared data must not leak sensitive information about the individuals to whom the data belongs or reveal their identity. Privacy-preserving data publishing is a research area that studies anonymizing person-specific data without compromising its utility for future data analysis. This talk discusses the inherent challenges of privacy protection and goes over some innovative anonymization solutions for three types of high-dimensional data: trajectory streams, static trajectories, and relational data. Results and insights from theoretical and experimental analysis are presented.
Speaker's Bio: Khalil Al Hussaeni is an Associate Professor of Computing Sciences at RIT Dubai. He received his Ph.D. degree in 2017 from the Faculty of Engineering and Computer Science, Concordia University, Montreal, Canada. His doctoral thesis proposed efficient and scalable techniques for anonymizing high-dimensional data. Dr. Al Hussaeni's Ph.D. thesis was ranked "Outstanding" (highest honor at the university level), and he was nominated for the Governor General's Gold Medal Award (most prestigious academic award across Canada).
Dr. Al Hussaeni's research interest goes under the umbrella of privacy-preserving data publishing. Particularly, this area of research targets anonymizing relational data, trajectories, data streams, and Big Data for various data mining tasks. He served as a reviewer for major venues, including The ACM International Conference on Information and Knowledge Management (CIKM), IEEE BigData, IEEE Transactions on Information Forensics and Security (TIFS), and IEEE Transactions on Knowledge and Data Engineering (TKDE).
Dr. Al Hussaeni was a member of the Data Mining and Security Laboratory research team at McGill University and a Research Assistant in the Computer Security Laboratory at Concordia University. He received his Master's degree in Information Systems Security in 2009 from Concordia Institute for Information Systems Engineering, Concordia University, Canada.