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：深度学习通过利用数据驱动的方法来管理和共享有限的频谱资源，为改进频谱接入技术提供了有前景的解决方案。对于其中许多应用而言，敏感的无线数据（如频谱图）存储于共享数据库或多利益相关方云环境中，因此容易遭受隐私泄露。本文旨在通过考察5G开放无线接入网（O-RAN）网络中共享数据库场景的代表性案例研究来解决此类隐私问题——在该网络中，近实时（near-RT）RAN智能控制器内设有一个共享数据库。我们专注于保护可供机器学习（ML）模型用于频谱共享和干扰缓解应用的数据，同时不损害模型和网络性能。其核心理念是：(i) 利用基于洗牌的可学习加密技术对数据进行加密，随后 (ii) 采用定制的视觉Transformer（ViT）作为训练好的ML模型，该模型能够对此类加密数据进行精确推理。本文提供了详尽的分析，并与类似的卷积神经网络（CNN）及更深层架构（如ResNet-50）作为基线进行了比较。实验表明，所提出的方法在处理加密数据时，分类准确率和F1分数分别比基线CNN显著提高24.5%和23.9%。尽管更深层的ResNet-50架构在准确率上略有提升（增加4.4%），但所提出的方法减少了99.32%的参数，从而将预测时间提升了近60%。