Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning (1804.00308v3)

Abstract: 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. We also introduce a fast statistical attack that requires limited knowledge of the training process. Finally, we design a new principled defense method that is highly resilient against all poisoning attacks. We provide formal guarantees about its convergence and an upper bound on the effect of poisoning attacks when the defense is deployed. We evaluate extensively our attacks and defenses on three realistic datasets from health care, loan assessment, and real estate domains.

Overview of the Study on Poisoning Attacks and Countermeasures for Regression Learning

The paper "Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning" presents a comprehensive analysis of poisoning attacks on linear regression models and proposes novel defense mechanisms. As machine learning systems increasingly influence high-stakes decision-making in sectors such as healthcare, finance, and real estate, ensuring their robustness against malicious activities is paramount.

Summary of Contributions

The authors contribute significantly to the understanding of poisoning attacks and propose a theoretically-grounded optimization framework for attacking and defending linear regression models:

1. First Systematic Study: This work represents the first systematic investigation into poisoning attacks specifically targeting linear regression models. Previous research had predominantly focused on classification tasks.
2. Theoretically-Grounded Optimization Framework: An optimization framework tailored for linear regression is presented. This framework enhances the effectiveness of poisoning attacks by selecting feature values and response variables that maximize the regression model's prediction error.
3. Fast Statistical Attack: A novel, computationally efficient statistical attack is introduced, which requires minimal knowledge of the training process. This attack capitalizes on statistical properties of the data distribution to generate effective poisoning points.
4. New Defense Algorithm – TRIM: The authors design a robust defense algorithm named TRIM, which iteratively estimates regression parameters while selectively excluding data points with high residuals. This method offers formal guarantees on its convergence and an upper bound on the mean squared error (MSE) increase due to poisoning.
5. Extensive Evaluation: The effectiveness of the proposed attacks and defenses is rigorously evaluated against four regression models (OLS, LASSO, Ridge, and Elastic Net) across realistic datasets in the domains of healthcare, loan assessment, and real estate.

Poisoning Attacks: Methodologies and Performance

Optimization-Based Attacks

The paper details an optimization approach under two settings: white-box (full knowledge of the model and training data) and black-box (limited knowledge). This approach involves:

• Adapting feature and response values iteratively using gradient ascent.
• Leveraging initialization strategies such as boundary flip and interior flip.
• Employing different optimization objectives to maximize attack effectiveness.

In experiments, optimization-based attacks significantly outperformed baseline attacks, achieving up to a factor of 6.83 improvement in MSE over the baseline by Xiao et al. Moreover, the optimization framework could increase the MSE by up to a factor of 155.7 compared to unpoisoned models, highlighting the vulnerability of regression models to these sophisticated attacks.

Statistical Attack

The proposed statistical attack stands out for its efficiency and practical applicability. By sampling from a multivariate normal distribution, this attack achieves competitive results with minimal computation and without deep insights into the training process. For instance, on certain datasets, the statistical attack achieved performance comparable to optimization-based attacks, demonstrating its utility in resource-constrained scenarios.

Defense Mechanisms: Evaluations and Insights

Traditional Defenses

Traditional robust statistics methods such as Huber regression, RANSAC, and techniques like RONI were evaluated:

• Huber Regression and RANSAC: These methods, designed to be resilient against noise and outliers, were largely ineffective against poisoning attacks. In fact, Huber regression sometimes resulted in higher MSE than no defense at all.
• RONI: This method, which rejects points causing the largest decrease in model performance, also underperformed in the presence of sophisticated poisoning attacks.

The TRIM Algorithm

The newly proposed TRIM algorithm addresses the limitations of traditional methods by focusing on iterative exclusion of high-residual points, ensuring the model is trained on a subset of data least affected by poisoning. TRIM demonstrated superior robustness:

• On the healthcare dataset, TRIM managed to reduce the median MSE by 6.1% across all attacks.
• The defense often resulted in MSEs much lower than other robust regression algorithms, outperforming Huber by a factor of 131.8, RANSAC by 17.5, and RONI by 20.28 on one dataset.

Implications and Future Directions

The findings underscore urgent practical and theoretical implications for machine learning security:

• Practical Impact: In a healthcare case paper, poisoning attacks led to drastic changes in prescribed medication dosages, with potential real-world harm to patients. This illustrates the critical need for robust defenses in applications involving human safety.
• Theoretical Contributions: The paper paves the way for further exploration of poisoning attacks in other regression-based tasks and different learning paradigms.
• Future Research: It suggests avenues like extending the defense mechanisms to other supervised learning tasks, reinforcing online learning models, and enhancing the understanding of adversary capabilities in black-box settings.

Conclusion

This paper makes valuable contributions by advancing knowledge on the vulnerabilities of linear regression models to poisoning attacks and proposing effective countermeasures. The TRIM defense algorithm, in particular, marks a significant step forward in securing machine learning systems. The insights drawn from this paper are poised to inform future research and development of robust, trustable AI systems.