Machine Learning Integrity and Privacy in Adversarial Environments
Alina Oprea · 2021
Machine learning is increasingly being used for automated decisions in applications such as health care, finance, autonomous vehicles, and personalized recommendations. These critical applications require strong guarantees on both the integrity of the machine learning models and the privacy of the user data used to train these models. The area of adversarial machine learning studies the effect of adversarial attacks against machine learning models and aims to design robust defense algorithms. The main challenges in this space are the development of realistic adversarial models that consider the specifics of real-world applications, and the design of machine learning algorithms resilient against a wide range of threats.