Preventing Machine Learning Poisoning Attacks Using Authentication and Provenance

Understand

Recent research has successfully demonstrated new types of data poisoning attacks.

• To address this problem, some researchers have proposed both offline and online data poisoning detection defenses which employ machine learning algorithms to identify such attacks.
• In this work, we take a different approach to preventing data poisoning attacks which relies on cryptographically-based authentication and provenance to ensure the integrity of the data used to train a machine learning model.
• The same approach is also used to prevent software poisoning and model poisoning attacks.