Information Leakage of Neural Networks

As machine learning is becoming a cornerstone of society, it increasingly encounters the challenge of handling sensitive data. This issue is magnified when trained machine learning models are shared with external entities, e.g., open-source or via API, which raises the critical question: Can sensitive information be extracted from the shared models?In this talk, we will navigate the fascinating domain of information extraction attacks targeting trained machine learning models. We will dissect various attack vectors across different adversarial settings and their potential to compromise data. Additionally, we will discuss strategies to mitigate these attacks and showcase the effectiveness of such techniques.Finally, we will touch upon the legal aspects and the importance in bridging between legal and technical definitions of risk.