Last week, the third annual CyberSec&AI Connected was held virtually. There were many sessions that combined academic and industry researchers along with leaders from Avast to explore the intersection of security and privacy and how AI and machine learning (ML) fit into both arenas. The conference strives to deepen the ties between academia and industry and this report for Avast’s blog dives into new and exciting work being done in various fields.
One of the speakers was Dawn Song, a computer science professor at the University of California at Berkeley. She outlined a four-part framework for responsible data use by AI that includes:
- Secure computing platforms, such as the Keystone open source secure processor hardware,
- Federated learning, whereby one’s data stays under their control,
- Differential privacy, using tools such as the Duet programming language and public data sets such as the Enron email collection, and
- Distributed ledgers that can have immutable logs to help guarantee security.