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

TL;DR

SecureV2X is a scalable, privacy-preserving system for V2X applications that significantly improves the efficiency and speed of secure neural network inferences in autonomous vehicle networks.

Contribution

It introduces a novel multi-agent system for secure neural network inference in V2X, enhancing scalability, speed, and privacy preservation over existing methods.

Findings

SecureV2X is 9.4 times faster than baseline systems.

It requires 143 times fewer computational rounds.

Achieves nearly 100 times faster runtime in object detection tasks.

Abstract

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 $9.4 \times$ faster, requires $143\times$ fewer computational rounds, and involves $16.6\times$ less communication on drowsiness detection compared to other secure systems. Moreover, it achieves a runtime nearly $100\times$ faster than state-of-the-art benchmarks in object detection tasks for red light violation detection.