Abstract
With the increasing deployment and growing popularisation of autonomous unmanned aerial vehicle (UAVs), such as surveillance, package delivery, and environmental monitoring, the need for efficient and secure UAV path planning algorithms has gained a high level of attention and importance. However, existing path planning methodologies often overlook strict security measures, resulting in sensitive information, from both civilian and even military use scenarios, vulnerable to security attacks. Moreover, the involvement of multiple parties, each with its own set of sensitive information, raises concerns regarding data privacy and security. To bridge this gap, this paper presents a novel approach that employs Multi-Party Secure Computation (SMPC) techniques atop generic path planning algorithms to address these security and privacy challenges.
We propose SecuPath, a secure framework that leverages cryptographic protocols for secure communication and computation, enabling multiple entities to jointly compute optimal drone paths without revealing their private inputs to any party. By integrating this privacy-preserving layer onto generic drone path planning algorithms, we ensure the optimality of the planning process while significantly elevating the privacy and security standards of UAV path planning operations. This framework not only preserves the confidentiality and privacy of sensitive data, but also fosters collaboration in scenarios where data sharing is essential, such as in urban airspace management or disaster response. Moreover, we analyse the communication and computation overhead introduced by the SMPC and demonstrate the protocol remains practical and efficient.
This research contributes to the growing field of work addressing privacy concerns in drone applications and provides a foundation for the development of secure and collaborative UAV systems. The integration of SMPC techniques with generic drone path planning algorithms sets the groundwork for privacy-preserving drone operations with wide applications across diverse domains with a balance between robust security measures and operational feasibility.
This paper is supported by Australian Research Council (ARC) Discover Project DP220101234.
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Shen, Y., Liu, J., Yuan, X., Sun, S., Cui, H. (2024). SecuPath: A Secure and Privacy-Preserving Multiparty Path Planning Framework in UAV Applications. In: Zhu, T., Li, Y. (eds) Information Security and Privacy. ACISP 2024. Lecture Notes in Computer Science, vol 14897. Springer, Singapore. https://doi.org/10.1007/978-981-97-5101-3_12
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