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

arXiv: https://arxiv.org/abs/2402.09710 · HTML: https://mlsanity.com/paper/2402.09710/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 15 Feb 2024.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Intriguing Properties of Vision Transformers](https://mlsanity.com/paper/2105.10497/) (2021)
- [The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining](https://mlsanity.com/paper/2203.07320/) (2022)
- [Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues](https://mlsanity.com/paper/1809.08707/) (2018)
- [SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning](https://mlsanity.com/paper/2005.10296/) (2020)
- [Dual Attention-Based Federated Learning for Wireless Traffic Prediction](https://mlsanity.com/paper/2110.05183/) (2021)
- [Privacy-Preserving Image Classification Using Vision Transformer](https://mlsanity.com/paper/2205.12041/) (2022)
- [ChARM: NextG Spectrum Sharing Through Data-Driven Real-Time O-RAN Dynamic Control](https://mlsanity.com/paper/2201.06326/) (2022)
- [An Analysis of Complex-Valued CNNs for RF Data-Driven Wireless Device Classification](https://mlsanity.com/paper/2202.09777/) (2022)
- [SAS-Assisted Coexistence-Aware Dynamic Channel Assignment in CBRS Band](https://mlsanity.com/paper/1805.06053/) (2018)
- [TrustSAS: A Trustworthy Spectrum Access System for the 3.5 GHz CBRS Band](https://mlsanity.com/paper/1907.03136/) (2019)
- [RiSi: Spectro-temporal RAN-agnostic Modulation Identification for OFDMA Signals](https://mlsanity.com/paper/2211.12287/) (2022)

## Built on by (most cited first, 2 of 2)

- [ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities](https://mlsanity.com/paper/2604.01239/) (2026)
- [LLM-AUG: Robust Wireless Data Augmentation with In-Context Learning in Large Language Models](https://mlsanity.com/paper/2604.17770/) (2026)

## Abstract (arXiv)

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%.