Abstract Gradient Training: A Unified Certification Framework for Data Poisoning, Unlearning, and Differential Privacy

Summary

Abstract Gradient Training (AGT) is a unified framework for certifying a given model and training procedure against training data perturbations, covering bounded perturbations, removal of data points, and addition of new samples. It works by bounding the reachable set of parameters to establish provable parameter-space bounds, and it targets models trained with first-order optimization methods. The framework is presented as covering data poisoning, machine unlearning, and differential privacy.

Topics

• Adversarial machine learning

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