Unintended Interactions among ML Risks and Defenses

Background

Machine learning models are susceptible to a wide range of risks to security, privacy, and fairness. Several defenses have been proposed to mitigate these risks. However, defending against a specific risk can result in an unintended increase (or decrease) in susceptibility to other risks. Similarly when defenses against multiple risks are applied to a machine learning model, there could be conflicting interactions among among them. This blog article provides additional context for this work.

Conference/journal paper publications

Vasisht Duddu, Sebastian Szyller, N. Asokan: SoK: Unintended Interactions among Machine Learning Defenses and Risks. IEEE S&P 2024. arXiv preprint arXiv:2312.04542. (Distinguished paper award)
Sebastian Szyller, N. Asokan: Conflicting Interactions Among Protection Mechanisms for Machine Learning Models. AAAI 2023. arXiv preprint arXiv:2207.01991

Pre-Prints

Vasisht Duddu, Rui Zhang, N. Asokan: Combining Machine Learning Defenses without Conflicts.. arXiv preprint arXiv:2411.09776.

Software Library

Amulet: A Library for Evaluating interactions among Machine Learning Risks and Defeneses Code

Posters

SoK: Unintended Interactions among Machine Learning Defenses and Risks. pdf

Talks

SoK: Unintended Interactions among Machine Learning Defenses and Risks. pdf talk
Conflicting Interactions Among Protection Mechanisms for Machine Learning Models. pdf