We kindly invite you to our first Deep Learning meetup in 2024 on January 17, hosted by Dynatrace in the Icon Tower at Wien Hauptbahnhof (central station)! Our topic will be Differential Privacy for Machine Learning, hosted by Alexander Schindler, Senior Scientist in the Center for Digital Safety & Security.
Talk Abstract: Differential Privacy for Machine Learning
Machine Learning requires a lot of data to train effective models. Data owners might not be willing to share the data because of its private nature. Differential Privacy can help to quantify and enhance the privacy of sensitive data, when performing data analysis or machine learning. Companies including Apple, Google, Microsoft and even governments employ Differential Privacy to gain insights into the data while preserving the privacy of individual users. But what is Differential Privacy? How can it make machine learning more private? Most importantly, does it actually always guarantee the privacy of individuals? This talk will provide you with the answers and an understanding of why, when and how to use Differential Privacy in a meaningful way.
Please sign up here.