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CAPPAD: a privacy-preservation solution for autonomous vehicles using SDN, differential privacy and data aggregation

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Abstract

Autonomous Vehicles (AVs) and driverless cars which are equipped with communication capabilities, advanced sensing, and Intelligent Control Systems (ICS), aim to modernize the transportation system. It increases user satisfaction by enhancing personal safety, reducing infrastructure costs, decreasing environmental interruption, and saving time for passengers. On the other hand, in emergency cases when AVs require maintenance, their generated sensitive information (e.g., AV location, low brake fluid amount of an AV) should be shared with Road Side Units (RSUs) and other vehicles to address their problems and provide quality services. Despite its appealing benefits, sensitive data sharing carries security and privacy issues that trigger serious risks like unintentional physical accidents. If the privacy of the AV is breached and its sensitive data is unintentionally disclosed during data transmission, adversaries can misuse them and cause artificial accidents. Current studies in this area lack efficiency and cost-effectiveness. To fill this gap and reduce the number of potential accidents, this article proposes a new Context-Aware Privacy-Preserving method for Autonomous Driving (CAPPAD). In particular, the Software-Defined Networking (SDN) paradigm is employed to bring flexibility to AVs’ privacy management while its SDN controller runs a novel algorithm for privacy preservation. Depending on whether the data generated is sensitive or not and whether there is an emergency, the AV applies Differential Privacy (DP) or Data Aggregation (DA) as its privacy-preserving method. Finally, extensive simulations are performed through MININET-WIFI to show the performance of CAPPAD in terms of privacy-preserving degree, computational cost overhead, computational complexity overhead, and latency. We also compare it with other relevant well-known studies to show its superior effectiveness.

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Authors and Affiliations

  1. Institute of Artificial Intelligence, Shaoxing University, Shaoxing, China

    Mehdi Gheisari

  2. Department of Cognitive Computing, Institute of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India

    Mehdi Gheisari

  3. Department of Computer Science, Islamic Azad University, Damavand, Iran

    Mehdi Gheisari

  4. Department of Computer Science, University of Wah, Wah Cantt, 47040, Pakistan

    Wazir Zada Khan

  5. Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada

    Hamid Esmaeili Najafabadi

  6. School of Computer Science, University College Dublin (UCD), Belfield, Dublin 4, D04 Y8K6, Ireland

    Gavin McArdle & Hamidreza Rabiei-Dastjerdi

  7. Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China

    Yang Liu

  8. Department of Electrical Engineering, University of Santiago of Chile (USACH), Santiago, 9170124, Chile

    Christian Fernández-Campusano

  9. Department of Computer Science, Wenzhou-Kean University, Wenzhou, 325015, China

    Hemn Barzan Abdalla

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  1. Mehdi Gheisari
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Gheisari, M., Khan, W.Z., Najafabadi, H.E. et al. CAPPAD: a privacy-preservation solution for autonomous vehicles using SDN, differential privacy and data aggregation. Appl Intell 54, 3417–3428 (2024). https://doi.org/10.1007/s10489-023-04991-w

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