Dr. Olivera Kotevska, Oak Ridge National Laboratories
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.