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
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
Source code