Preserving Data Privacy for ML-driven Applications in Open Radio Access Networks

arXiv:2402.09710v1 Announce Type: new
Abstract: Deep learning offers a promising solution to improve spectrum access techniques by utilizing data-driven approaches to manage and share limited spectrum resources for emerging applications. For several of these applications, the sensitive wireless data (such as spectrograms) are stored in a shared database or multistakeholder cloud environment and are therefore prone to privacy leaks. This paper aims to address such privacy concerns by examining the representative case study of shared database scenarios in 5G Open …

access applications arxiv cs.cr cs.lg data database data-driven data privacy deep learning emerging manage networks privacy radio resources sensitive share solution spectrum techniques wireless