Privacy-Preserving Compressive Sensing for Urban Traffic Monitoring
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Solution Overview
Problem
Conventional urban traffic congestion estimation methods are costly, inefficient, and fail to protect vehicle data privacy, especially with the increasing complexity of urban road networks and the need for rapid data processing.
Innovation Solution
A real-time urban traffic status monitoring method using privacy-preserving compressive sensing, which divides vehicle data into parts processed by roadside units and encrypted using secure two-party computation protocols, then outsourced to cloud platforms for accurate and efficient traffic estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vehicle data is outsourced to cloud platforms for traffic monitoring, then data processing capability is improved, but data privacy protection deteriorates
Solution Approach 1:
The patent divides vehicle data into two separate parts: encrypted traffic status data and decryption keys. The encrypted data is outsourced to cloud platforms for processing, while the decryption keys remain locally stored in roadside units. This segmentation allows the cloud to process data without accessing the actual sensitive information, thereby maintaining privacy protection while enabling large-scale data processing capabilities.
2Object-affected harmful factors
If data encryption algorithms are applied to protect privacy, then data security is improved, but operation efficiency deteriorates
Solution Approach 1:
The patent applies encryption algorithms to vehicle data in advance, before the data is uploaded to cloud platforms. The roadside units encrypt the traffic status data and store the encrypted versions in the cloud, while retaining the decryption keys locally. This preliminary encryption action ensures that all subsequent data processing operations on the cloud occur on already-encrypted data, eliminating the need for repeated encryption/decryption cycles and thus maintaining high operation efficiency while ensuring continuous security.
3Measurement precision
If all road data is collected for accurate traffic estimation, then measurement precision is improved, but time delay increases
Solution Approach 1:
The patent extracts only the essential traffic status information from vehicle data, such as position, speed, and direction, while excluding unnecessary detailed information. The roadside units filter and extract these key parameters before encrypting and uploading them to the cloud. This extraction process reduces the volume of data that needs to be collected and processed, enabling faster data collection and processing times while maintaining sufficient accuracy for traffic estimation purposes.
Data Source
AI summary
The present disclosure relates to a realtime urban traffic status monitoring method based on privacy-preserving compressive sensing, including the following steps: step S1: dividing vehicle data under privacy preserving into two parts, and sending the two parts to two different road side units (RSU) for preprocessing; step S2: outsourcing, by the two different RSUs, preprocessed vehicle data to two cloud platforms (CP) respectively, and designing a data encryption execution protocol based on a finally expected operation result and interactive operation between the two CPs, to encrypt the data; and step S3: receiving, by a navigation service provider (NSP), encrypted data from the CPs, decrypting the received encrypted data, and estimating an urban traffic status by using a compressive sensing technology. The present disclosure can remarkably enhance a capability of protecting privacy of vehicle data, ensure rapid and accurate data processing, reduce energy consumed for urban traffic estimation, and shorten required traffic estimation time.