Assessing and Mitigating Privacy Risks in Machine Learning and Data-Intensive Environments

• Manolis Terrovitis (ATHENA RC)

This webinar introduces the key privacy challenges arising from machine learning and data-intensive applications. Participants will explore the principles of data anonymization, pseudonymization, and differential privacy, along with common privacy attacks targeting datasets and AI models. Through practical examples and a hands-on session with the Amnesia anonymization platform, attendees will learn how to assess and mitigate privacy risks while developing compliant and privacy-preserving AI solutions.