Privacy-Preserving Statistical Data Generation: Application to Sepsis Detection

arXiv:2404.16638v1 Announce Type: cross
Abstract: The biomedical field is among the sectors most impacted by the increasing regulation of Artificial Intelligence (AI) and data protection legislation, given the sensitivity of patient information. However, the rise of synthetic data generation methods offers a promising opportunity for data-driven technologies. In this study, we propose a statistical approach for synthetic data generation applicable in classification problems. We assess the utility and privacy implications of synthetic data generated by Kernel Density Estimator and K-Nearest …

application artificial artificial intelligence arxiv biomedical cs.cr cs.lg data data-driven data protection detection information intelligence legislation opportunity privacy protection regulation sectors study synthetic synthetic data technologies