Preserving Data Privacy for ML-driven Applications in Open Radio Access
  Networks
Pranshav Gajjar Azuka Chiejina Vijay K. Shah
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 Radio
Access Network (O-RAN) networks where we have a shared database within the
near-real-time (near-RT) RAN intelligent controller. We focus on securing the
data that can be used by machine learning (ML) models for spectrum sharing and
interference mitigation applications without compromising the model and network
performances. The underlying idea is to leverage a (i) Shuffling-based
learnable encryption technique to encrypt the data, following which, (ii)
employ a custom Vision transformer (ViT) as the trained ML model that is
capable of performing accurate inferences on such encrypted data. The paper
offers a thorough analysis and comparisons with analogous convolutional neural
networks (CNN) as well as deeper architectures (such as ResNet-50) as
baselines. Our experiments showcase that the proposed approach significantly
outperforms the baseline CNN with an improvement of 24.5% and 23.9% for the
percent accuracy and F1-Score respectively when operated on encrypted data.
Though deeper ResNet-50 architecture is obtained as a slightly more accurate
model, with an increase of 4.4%, the proposed approach boasts a reduction of
parameters by 99.32%, and thus, offers a much-improved prediction time by
nearly 60%.