Dr. Olivera Kotevska of Oak Ridge National Laboratories, TN

Abstract: Today we face an explosion of systems from health monitoring to national security infrastructure that generate and collect vast data daily. Increasingly, these systems use machine learning methods for intelligent decisions, prone to cyber-security attacks. So, we ask how data privacy should be protected in a world where data is gathered and shared with increasing speed and ingenuity. This presentation will describe several privacy techniques for streaming data protection, frameworks for machine learning, and privacy attacks. We will share results using real-world datasets and ORNL testbed and describe best practices. The talk concludes with a brief discussion of present open challenges in privacy-preserving algorithms and how the research findings can be transferred to industry.

Biography:

Bio: Olivera Kotevska is a Research Scientist in Mathematics in Computations Section in the Computer Science and Mathematics Division (CSMD). She received her PhD in Computer Science from University of Grenoble Alpes, France in 2018. Her research is in privacy algorithms and machine learning for energy and national security applications. She has published and regularly serves as a program committee member at the top conferences in these domains and served in organizational roles including Co-Chair of IEEE Big Data Industry and Government Program, Chair of IEEE Power and Energy Society Computational Analytical Methods Subcommittee, and Co-Editor of Sensors journal special issue IoT Data Analytics. She is an organizer and chair of IEEE WiE East TN affinity group. She received IEEE Senior membership award in ’21 and ORNL CSMD Outreach and Service award in ’20.

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