Data Minimization

Data minimization aims to reduce the amount of data used in machine learning, balancing privacy protection with model accuracy. Current research focuses on developing optimization algorithms, including bio-inspired methods and integer programming techniques, to select minimal yet informative datasets for training and inference. This work is crucial for addressing privacy concerns arising from data breaches and complying with data protection regulations, impacting both the development of responsible AI and the practical application of machine learning in sensitive domains.