# SecureV2X: An Efficient and Privacy-Preserving System for Vehicle-to-Everything (V2X) Applications

arXiv: https://arxiv.org/abs/2508.19115 · HTML: https://mlsanity.com/paper/2508.19115/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 26 Aug 2025.

## Builds on (most cited first, 8 of 8)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Gazelle: A Low Latency Framework for Secure Neural Network Inference](https://mlsanity.com/paper/1801.05507/) (2018)
- [CrypTen: Secure Multi-Party Computation Meets Machine Learning](https://mlsanity.com/paper/2109.00984/) (2021)
- [CrypTFlow2: Practical 2-Party Secure Inference](https://mlsanity.com/paper/2010.06457/) (2020)
- [Protecting Privacy of Users in Brain-Computer Interface Applications](https://mlsanity.com/paper/1907.01586/) (2019)
- [A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEG](https://mlsanity.com/paper/2106.00613/) (2021)
- [Visor: Privacy-Preserving Video Analytics as a Cloud Service](https://mlsanity.com/paper/2006.09628/) (2020)
- [L-SRR: Local Differential Privacy for Location-Based Services with Staircase Randomized Response](https://mlsanity.com/paper/2209.15091/) (2022)

## Abstract (arXiv)

Autonomous driving and V2X technologies have developed rapidly in the past decade, leading to improved safety and efficiency in modern transportation. These systems interact with extensive networks of vehicles, roadside infrastructure, and cloud resources to support their machine learning capabilities. However, the widespread use of machine learning in V2X systems raises issues over the privacy of the data involved. This is particularly concerning for smart-transit and driver safety applications which can implicitly reveal user locations or explicitly disclose medical data such as EEG signals. To resolve these issues, we propose SecureV2X, a scalable, multi-agent system for secure neural network inferences deployed between the server and each vehicle. Under this setting, we study two multi-agent V2X applications: secure drowsiness detection, and secure red-light violation detection. Our system achieves strong performance relative to baselines, and scales efficiently to support a large number of secure computation interactions simultaneously. For instance, SecureV2X is 9.4 × faster, requires 143× fewer computational rounds, and involves 16.6× less communication on drowsiness detection compared to other secure systems. Moreover, it achieves a runtime nearly 100× faster than state-of-the-art benchmarks in object detection tasks for red light violation detection.