In the open and dynamic low-altitude intelligent network, unmanned aerial vehicle (UAV) operational data sharing faces security threats such as data eavesdropping, replay attacks, and unauthorized access, which may seriously jeopardize low-altitude operation security. It is crucial to guarantee the secure sharing of UAV operational data. Existing schemes utilize identity-based broadcast encryption to achieve point-to-multipoint data security sharing. Nevertheless, the sharing flexibility is sometimes restricted by these schemes' requirement to preset the sharing group capacity. Moreover, in the decryption process, the sharer needs to perform additional mathematical operations for other group members, which leads to a heavy burden of decryption computation. In this paper, we propose a secure sharing scheme for low-altitude UAV operational data based on broadcast proxy re-encryption. Through ciphertext re-encryption, the suggested technique creates a hybrid encryption mechanism that converts point-to-point sharing into point-to-multipoint sharing by utilizing identity-based broadcast encryption and symmetric encryption. In point-to-multipoint sharing, the scheme supports stateless data group sharing with fixed-length private keys and without preset group capacity, taking into account the security, flexibility, and efficiency of sharing. Experimental results show that in point-to-point sharing, the encryption and decryption computation overhead is fixed; in point-to-multipoint sharing, compared with the suboptimal scheme, the re-encryption key generation overhead is reduced by more than 40%, and the re-encryption ciphertext decryption algorithm costs about 3.6 milliseconds.

In response to the significant impact of speckle noise on the detection accuracy of synthetic aperture radar (SAR) image changes, the high network model complexity of existing capsule network-based image change detection methods, and the loss of a large amount of original image information in training samples, this paper proposed a self-supervised image change detection method based on the light capsule network (SLCapsNet). The logarithmic ratio operator difference graph was generated, and the “pseudo label” of training samples with high confidence was obtained through the maximum inter-class variance method and fuzzy C-means clustering method, which laid the foundation for self-supervised learning. The paper constructed a three-channel training sample based on the two temporal SAR images and difference graph of logarithmic ratio operators to maximize the preservation of sample information. Lightweight capsule network was designed to extract training sample features through single scale convolution, and a single scale capsule network was used to mine spatial relationships between features. Comparative experiments and ablation experiments were set up, and tests were conducted on five real SAR datasets. The experimental results show that the advantage of the proposed method is to improve the operational efficiency of the method while reducing model complexity, obtain stronger robust features, suppress the adverse impact of speckle noise on change detection performance, and improve change detection performance.