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%.

翻译：深度学习通过采用数据驱动的方法来管理和共享有限频谱资源，为提升频谱接入技术提供了前景广阔的解决方案，以支持新兴应用。对于其中若干应用而言，敏感无线数据（如频谱图）存储于共享数据库或多利益相关方云环境中，因而易于发生隐私泄露。本文旨在通过研究5G开放无线接入网络（O-RAN）中共享数据库场景的典型案例来应对此类隐私问题，该场景在近实时（near-RT）无线接入网络智能控制器内设有共享数据库。我们聚焦于保护可供机器学习（ML）模型用于频谱共享与干扰抑制应用的数据安全，同时不损害模型与网络性能。其核心思想是：（i）采用基于乱序的可学习加密技术对数据进行加密，随后（ii）部署定制化的视觉Transformer（ViT）作为训练好的ML模型，该模型能够对此类加密数据执行精确推理。本文进行了详尽分析，并以同类卷积神经网络（CNN）及更深层架构（如ResNet-50）作为基线进行对比。实验表明，所提方法在加密数据上运行时，其百分比准确率与F1分数分别较基线CNN显著提升24.5%与23.9%。尽管更深的ResNet-50架构获得了略高4.4%的准确率，但所提方法的参数量减少了99.32%，从而将预测时间大幅提升近60%。