Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning

Understand

As machine learning becomes widely used for automated decisions, attackers have strong incentives to manipulate the results and models generated by machine learning algorithms.

• In this paper, we perform the first systematic study of poisoning attacks and their countermeasures for linear regression models.
• In poisoning attacks, attackers deliberately influence the training data to manipulate the results of a predictive model.
• We propose a theoretically-grounded optimization framework specifically designed for linear regression and demonstrate its effectiveness on a range of datasets and models.