SecureV2X: Privacy-Preserving V2X Inference

• SecureV2X is a cryptographically secure V2X framework that employs multi-party computation to enable neural network inference without exposing private data.
• It interposes secure multi-agent computation modules between vehicular clients and model servers using techniques like additive secret sharing and Beaver’s triples.
• The system demonstrates significant performance gains and scalability in real-time applications such as driver drowsiness detection and red-light violation detection.

SecureV2X refers to a system and methodology for enabling efficient, scalable, and privacy-preserving neural network inference in Vehicle-to-Everything (V2X) applications through cryptographically secure protocols. Designed to address both the privacy risks arising from sensitive vehicular and driver data, as well as the efficiency bottlenecks inherent in secure machine learning deployments, SecureV2X interposes secure multi-agent computation modules between server-side service providers (model owners) and vehicular clients (data owners), ensuring that neither party’s private data is ever revealed in plaintext throughout the computation lifecycle (Lee et al., 26 Aug 2025).

1. SecureV2X System Architecture and Threat Model

SecureV2X implements a multi-party computation (MPC) paradigm, where privacy is attained via secret sharing of data and model parameters between the client (e.g., a vehicle, traffic camera, or edge device) and a service provider (e.g., a machine learning inference server holding proprietary network weights). Both input features (such as driver EEG signals or traffic camera images) and model weights are decomposed into additive shares over a cryptographic ring $\mathbb{Z}_{2^L}$, and computations proceed such that neither participant can individually reconstruct the other's input.

Communication between client and server is orchestrated using frameworks such as CrypTen, utilizing well-studied cryptographic primitives:

• Additive secret sharing: Ensuring that the sum of all shares reconstructs the full data, while individual shares are statistically independent of the input.
• Beaver's triples: For secure multiplication, enabling nonlinear functions (e.g., convolutions, fully connected layers) without interaction beyond the exchange of cryptographically masked multiplication results.
• Pseudo-random zero-sharing primitives: To reduce pre-processing and online communication.
• Arithmetic-binary conversion: Secure protocols allow for conversions between arithmetic and binary secret shares to implement efficient activation functions and quantization throughout the model.

The overall system is modular, supporting deployment at edge, in-vehicle, or cloud endpoints, and is evaluated under multi-agent settings with numerous clients interacting concurrently.

2. Cryptographic Protocols and Secure Inference Workflow

SecureV2X translates end-to-end neural inference pipelines into a sequence of cryptographically secure sub-protocols. The computation is articulated as follows:

1. Secret Sharing Stage: The client secret-shares its private input $x$ (e.g., EEG vector or image tensor), and the server secret-shares neural network weights $W$.
2. Secure Linear Operations: Additions and scalar multiplications proceed locally on secret shares with no communication, maximizing efficiency.
3. Secure Nonlinear Operations/Multiplications: For operations such as matrix multiplication or convolutions, Beaver's triples are employed. The parties pre-share random triples $(a,b,c)$ such that $c=ab$:

$$[x][y] = [a][b] + ([x-a][b] + [a][y-b]) + ([x-a][y-b])$$

where $[\cdot]$ denotes an additive share.

1. Activation and Binary Conversion: Nonlinearities (e.g., ReLU) and quantization steps adapt secure arithmetic-to-binary conversion protocols, enabling efficient implementation of step functions required for model inference in quantized NNs.
2. Result Reconstruction: At the protocol's conclusion, only the agreed output (e.g., classification result or bounding boxes) is revealed to the authorized party, with all intermediate and auxiliary information kept secret.

This methodology is consistently applied in both the CryptoDrowsy (drowsiness detection) and FastSec-YOLO (object detection) modules, parametrized for the specifics of the respective models.

3. Privacy Guarantees and Data Protection

SecureV2X enforces strong cryptographic privacy:

• Input Confidentiality: Sensitive client-side data (e.g., imagery revealing drivers or locations, medical EEG signals) is never exposed in unencrypted or reconstructible form to the model holder.
• Model Confidentiality: Proprietary or regulated NN weights are kept secret from the client, even though the collaborative protocol yields a prediction.
• Intermediate Privacy: All intermediate values during inference (feature maps, hidden units, etc.) are cryptographically split between the parties.
• Regulatory Alignment: The privacy properties meet key regulatory requirements (such as those articulated by USDOT) for deployed V2X systems, particularly with respect to biometric and location privacy.

Only the result—the inference output returned to the appropriate party—is exposed; all other artifacts are unobservable by construction.

4. Applications and Deployment Scenarios

SecureV2X supports and has been evaluated in two archetypal V2X use cases:

Module	Task	Data Modality	Threat Model	Features	Performance
CryptoDrowsy	Driver drowsiness detection	EEG signals	Protects medical/biometrics	Secure compact CNN over 1×384 input	$9.4\times$ faster, $143\times$ fewer rounds, $16.6\times$ less communication than secure baselines
FastSec-YOLO	Red-light violation detection	Images	Location, identity privacy	Secure YOLOv5n object detection	Nearly $100\times$ faster than prior secure object detectors

Editor's term: SecureV2X's architecture allows it to scale efficiently to support numerous independent client-server secure computations, which is essential in high-density urban deployments or traffic intersection monitoring.

5. Performance, Scalability, and System Evaluation

Quantitative evaluation demonstrates outstanding gains over prior secure inference baselines:

• Secure Drowsiness Detection: Approximately $9.4\times$ faster overall inference, $143\times$ reduction in computation rounds, and $16.6\times$ lower communication cost compared to previous secure computation systems in V2X scenarios.
• Red-Light Violation Detection: Achieved nearly $100\times$ speedup over state-of-the-art secure object detection benchmarks, with minimal impact on detection accuracy.
• Multi-Agent Scalability: Experiments verify that average per-agent inference time increases only marginally as the number of parallel secure computations scales. This suggests the protocols are well suited for deployment in modern connected traffic infrastructure.

The improvements stem from protocol-level optimizations, the use of efficient secure primitives, and careful batching and parallelization.

6. Practical Implications and Future Extensions

The integration of SecureV2X into V2X systems provides:

• Real-time, privacy-preserving analytics for smart transit and driver safety without risk of unauthorized location, medical, or visual data exposure.
• Deployment compatibility with both edge and cloud resources, facilitating secure ML at roadside units, traffic cameras, and in-vehicle devices.
• Foundational protocols that are adaptable to broader secure ML tasks beyond those evaluated, contingent upon the secure composition and arithmetic structure of the neural models.
• Alignment with regulatory and trust requirements as articulated in recent national V2X deployment plans, particularly where end-user privacy is non-negotiable.

A plausible implication is that the scalable, efficient protocol design will enable additional privacy-critical V2X applications—such as federated learning, driver state monitoring, or secure cooperative perception—by adapting core protocol modules to further neural architectures or sensor modalities.

7. Summary Table: Key Properties

Dimension	SecureV2X Characteristic	Quantitative Result/Description
Privacy	Data/model protection	Secret sharing, no plaintext disclosure
Efficiency	Communication/computation	$9.4-100\times$ faster, $16.6\times$ lower comm.
Scalability	Multi-agent capability	Marginal increase in per-agent overhead
Applicability	Drowsiness, object detection	Secure CNN, secure YOLOv5n modules
Compliance	Regulatory alignment	Conforms to privacy mandates

SecureV2X thus constitutes a cryptographically robust, highly efficient, and privacy-preserving architecture for advanced V2X intelligence—enabling neural inference and analytics in next-generation intelligent transportation systems while avoiding the compromise of sensitive data (Lee et al., 26 Aug 2025).