The Research Institute – Digital Human Rights Center and SBA Research offer a specialist seminar addressing critical security and legal issues in artificial intelligence (AI). Aimed at organizations developing or deploying AI systems, the seminar focuses on ensuring compliance with regulatory standards, enhancing security measures, and improving AI literacy among staff.
Core Areas:
- Privacy & Security Risks: Addresses threats such as poisoning and inference attacks in machine learning.
- Technical Protections: Covers techniques like anonymization, differential privacy, and homomorphic encryption.
- Legal Framework: Explains the AI Act and recent guidance from the European Data Protection Board.
- Practical Application: Includes real-world examples and interactive exercises to integrate learning into daily work.
The seminar combines legal insights with advanced information security expertise to help organizations optimize the compliance and security of their AI systems. It aligns with the AI Act’s requirement for AI literacy among staff and offers actionable knowledge for professionals.
SBA Speakers
Anastasia Pustozerova is a researcher at SBA Research specializing in privacy and security in machine learning, with a focus on federated learning and differential privacy. She holds a bachelor’s degree in Applied Mathematics and Physics from St. Petersburg University and a joint master’s degree in Computational Logic from TU Wien, TU Dresden, and Free University of Bozen-Bolzano. Since 2020, she has lectured on Federated Learning Privacy and Security at St. Pölten University of Applied Sciences. Currently pursuing a PhD at TU Wien, her research aims to address privacy threats and develop mitigation strategies in federated learning.
Tanja Šarčević is a Machine Learning Privacy and Security researcher at SBA Research and a PhD candidate at TU Wien. She holds a bachelor’s degree in Computer Science from Zagreb University and a master’s in Logic and Computation from TU Wien. Tanja lectures at TU Wien and FH Technikum Wien on privacy and security in machine learning. Her research focuses on trustworthy machine learning, with an emphasis on data privacy and ownership protection.