Privacy-preserving Cyber-Safe Machine Learning Models for Traffic Forecasting

Traffic congestion not only disrupts transportation systems but also poses significant cybersecurity risks, particularly in protecting sensitive driver data. Accurate traffic forecasting is essential to mitigate these disruptions, yet traditional approaches often compromise privacy and expose critical data to potential threats. Leveraging raw driver data without adequate security measures can lead to privacy breaches and cybersecurity vulnerabilities. We propose a novel approach utilizing advanced cryptographic techniques on encrypted data to predict traffic conditions without compromising individual driver location privacy. Our technique leverages cutting-edge quadratic functional encryption to construct a semi-private neural network. This network consists of two parts: a private section that handles encrypted location reports, aggregates them, and generates outputs for subsequent non-encrypted layers. These layers enhance forecasting performance while minimizing storage, computation, and bandwidth requirements. Functional encryption serves as the cryptographic foundation for achieving these goals. First, we establish a comprehensive, spatiotemporal route format with unique encrypted identifiers for each route segment. Second, we analyze the impact of privacy levels on accuracy, acknowledging the trade-off between privacy and precision. This project also integrates an adaptable framework to account for dynamic traffic changes due to accidents, events, or weather, with a strong focus on maintaining data security throughout. We recognize that privacy-preserving techniques may increase user-side computation and communication overhead, potentially impacting system performance. Therefore, we will investigate the trade-offs between accuracy, resource allocation, and cybersecurity. We request a one-year extension to thoroughly explore encryption techniques and their impact on performance and privacy, ensuring a robust solution for privacy-preserving traffic forecasting.

• Record URL:
  • https://utc.engineering.osu.edu/privacy-preserving-machine-learning-models-traffic-forecasting

Language

• English

Project

• Status: Active
• Funding: $450,000
• Contract Numbers:

  69A3552348327

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Center for Automated Vehicle Research with Multimodal Assured Navigation

  Ohio State University
  Columbus, OH  United States  43210
• Project Managers:

  Kline, Robin

• Performing Organizations:

  North Carolina A&T State University

  1601 E. Market Street
  Greensboro, NC  United States  27411
• Principal Investigators:

  Mahmoud, Mahmoud

• Start Date: 20231030
• Expected Completion Date: 20250831
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Data privacy; Drivers; Information management; Machine learning; Neural networks; Traffic forecasting
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01901374
• Record Type: Research project
• Source Agency: Center for Automated Vehicle Research with Multimodal Assured Navigation
• Contract Numbers: 69A3552348327
• Files: UTC, RIP
• Created Date: Dec 4 2023 5:03PM