This talk focuses on how new technologies and applications can have far reaching implications regarding the control we have over the data created by/about us. The primary found will be a discussion around the application of accelerometer data for medical purposes and how those implications effect all users of devices with accelerometer capabilities. To frame the discussion, I will present findings produced using simple machine learning methods, building on prior research in academia. I will move on to explore the idea of "privacy by design" and how the goals of privacy align with security. Finally I will invite a discussion on how these implications can be applied to other contexts, and how professionals view privacy based on personal beliefs vs organizational needs.
Self: Graduated Carnegie Mellon with a master's in privacy engineering (MSIT-PE program) and am IAPP/CIPT certified.