New views on multimedia data: Privacy and other reasons for research on data minimization.
published: Jan. 29, 2019, recorded: January 2019, views: 7
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Conventionally, multimedia researchers have adhered to the maxim “There is no data like more data”. However, the new General Data Protection Regulation, which came into force in Europe in 2018, has caused the data science world to shift its perspective. If there are constraints on how data can be gathered and used, then researchers must adapt themselves to design effective algorithms and systems under those constraints. In this talk, we look at how “data greed” has characterized past research on analyzing multimedia content and discover why it is time to revisit our assumption that more data will lead to better performance of algorithms. The talk covers examples of research on data dropping and data bleaching that make the case for the potential of data minimization, and motivation the need for future work in that direction. Finally, we point out that techniques focusing on how to perturb or remove information in multimedia data have a potential to protect the privacy of users, without requiring them to stop sharing their images and videos online.
Download slides: multimediamodeling2019_larson_data_minimization_01.pdf (4.1 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !