With the continuous development of UAV technology, the application of UAV swarm has gradually become the focus of research all over the world. Although UAV swarm provides some advantages in terms of autonomous collaboration, the traditional UAV management technology suffers from security challenges, including the risk of single points of failure due to centralized control, which makes UAV swarm susceptible to hacker attacks. Due to some advantages of blockchain, such as decentralization, tamper-proof characteristics, and traceability, it is applied to the drone swarm to solve some security challenges brought about by centralized management. However, blockchain cannot achieve secure access control on the data it stores, which may leak some crucial data. Therefore, a secure and efficient access-control model based on blockchain and ciphertext-policy attribute-based encryption (CP-ABE) is proposed, and a secure data-access scheme is designed under this model, which can not only prevent the leakage of critical data but also realize lightweight access control. Moreover, to improve the decryption efficiency of the data user, an outsourcing-based data decryption scheme is also studied, in which the complex calculations are completed by the data user agency. The experiments show that when the number of attributes is 60, the computation cost of the proposed scheme is 0.404 s, which is much lower than the existing research, and is more suitable for the UAV swarm with limited computing power. Moreover, the communication cost of the proposed scheme is reduced by about 30% compared with the existing scheme under the same conditions. The security analysis also shows that the proposed scheme is secure and reliable, and can resist a variety of attacks such as collusion attacks, man-in-the-middle attacks, and forgery attacks.


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    Title :

    A Secure and Efficient Access-Control Scheme Based on Blockchain and CP-ABE for UAV Swarm


    Contributors:
    Pengbin Han (author) / Aina Sui (author) / Jiang Wu (author)


    Publication date :

    2025




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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