Intel Corporation
UTILIZING DATA PROVENANCE TO DEFEND AGAINST DATA POISONING ATTACKS

Abstract:

The present invention discloses a secure ML pipeline to improve the robustness of ML models against poisoning attacks and utilizing data provenance as a tool. Two components are added to the ML pipeline, a data quality pre-processor, which filters out untrusted training data based on provenance derived features and an audit post-processor, which localizes the malicious source based on training dataset analysis using data provenance.