Ciphertext-Policy Attribute-Based Encryption (CP-ABE) schemes with user revocation allow for dynamic updates to users' access rights. However, existing schemes often encounter issues such as low re-encryption efficiency, inflexible access control, and susceptibility to collusion attacks. To address these challenges, we propose UR-CP-ABE, an efficient user revocation scheme built upon a double encryption method. Specifically, UR-CP-ABE stores both valid and revoked users' identity information in a binary tree. This design enables flexible user authority revocation by modifying only the binary tree's relevant secret sub-items. Moreover, the scheme limits the scope of revocation-induced binary tree updates to a single sub-item. This key optimization resolves the critical issue where re-encryption overhead scales linearly with the number of attributes of revoked users. In addition, we eliminate the possibility of attackers constructing secret sub-keys, preventing the collusion attacks. UR-CP-ABE also supports bidirectional revocation, allowing for the revocation and restoration of user rights, which is not available in other related schemes. Our theoretical analysis and experiments demonstrate that UR-CP-ABE has better performance than other schemes, especially in the re-encryption stages. Moreover, UR-CP-ABE has been proved to be secure based on the decisionalq-parallel BDHEhardness assumption in the standard model.

Specific emitter identification refers to identifying a specific emitter by its radio frequency fingerprint extracted from a given signal. Recently, most current methods for specific emitter identification are usually based on neural networks due to their great success. However, with the increasing complexity of radar systems, the neural networks of these methods are too shallow to extract distinctive fingerprint characteristics of modern emitters, especially for those of the same type, resulting in low identification accuracy. To address this challenge, this paper proposes a novel deep convolutional neural network named SepNet. We observed that the feature representation of each radar signal can be decomposed into two independent parts: a radio frequency fingerprint part capturing features of the specific emitter and a variation part capturing features of the signal content. Based on this insight, SepNet is designed to separate fingerprint information from raw signals and uses it to identify specific emitters with high accuracy. SepNet incorporates a separation block and a classification block to perform feature separation and emitter identification tasks, respectively. In addition, a reconstruction block is instrumented into SepNet to guide the separation process. Experimental results demonstrate that SepNet outperforms four end-to-end deep learning models in terms of identification accuracy, and ablation studies confirm the effectiveness of SepNet’s architecture.

With the growing demand for public data openness, sharing data while preserving data privacy has become a critical challenge. Traditional techniques, such as data anonymization and differential privacy, provide baseline privacy guarantees but face some limitations, including limited generalizability and utility degradation due to inappropriate perturbations. To overcome these limitations, this paper proposes a hybrid diffusion model for generating privacy-preserving tabular data. Unlike single-structure data generation models, our proposed approach integrates differential privacy with two lightweight generative models to effectively balance data privacy and data utility. Specifically, our approach consists of three phases: data preprocessing, privacy protection, and data generation. In the data preprocessing phase, the adaptive techniques are used for data normalization. During the privacy protection phase, Gaussian noise and randomized response mechanisms are applied to enhance data privacy. Finally, in the data generation phase, Gaussian diffusion is used for numerical attributes and multinomial diffusion for categorical attributes, which effectively handles the original data of mixed types. This design enhances both the stability of the generative model and the diversity of the synthetic data. Experiments on six public datasets demonstrate that although our approach incurs only a slight reduction in machine learning utility, measured by classification accuracy, F1 score, and regression R2scores, it greatly improves privacy metrics compared to the state-of-the-art tabular diffusion models.

As an effective measure to capture the object of interest in video sequence, video salient object detection (VSOD) requires the processing of information from spatial-motion modalities, although plenty of traditional VSOD models were dedicated to developing efficient spatial and motion features to obtain salient objects of global consistency, the highly redundant spatial information brought by consecutive identical objects will inevitably reduce the generalization ability of these VSOD model. Although exploring the integration of spatial and motion information can improve the inter-frame correlation of salient objects to some extent, previous models tend to focus only on simple spatio-temporal fusion, which can also lead to the generation of redundant information, resulting in poor detection performance. Therefore, it is necessary to focus on effectively fusing the feature information of different modalities to eliminate the effect of redundant information. In this research, we proposed a self-attentive multilayer cross-stacking fusion based VSOD model, which productively extracts the multimodal features for two-way information transfer, fully utilizes the spatial and temporal knowledge to complement each other, and refines the cross-stacking of the interacted information and spatial features for local and global saliency optimization. As a result, the redundant spatial information can be largely eliminated, reducing the misidentification of salient objects due to blurred backgrounds or moving objects, and adaptively activating more weights of the salient object to achieve globally consistent saliency. Comprehensive experiments on four publicly available VSOD datasets demonstrated that the model had superior performance compared to the latest multiple VSOD models.

Outsourcing data mining tasks is beneficial for data owners who either lack expertise in data mining or sufficient computing resources. However, directly releasing the original data would leak private information. Research on Privacy-Preserving Data Mining (PPDM) is dedicated to addressing this issue, the aim of this research is to reduce the risk of privacy violations and preserve the knowledge in the original data. However, most existing methods in the literature ignore the case in which service providers want to verify the integrity and authenticity of their clients’ data to avoid data tampering before performing data mining tasks. In this paper, a new method is proposed to extend the turtle shell algorithm of data hiding to protect the privacy of the original data and to acquire authentication functions simultaneously. The act of data perturbation is performed by replacing data values with their closest neighbors according to a reference matrix. Further, a message authentication code is hidden in the perturbed data to verify the integrity and authenticity of the perturbed data. The experimental results showed that the proposed method achieved the purpose of data perturbation and outperformed similar methods in satisfying the PPDM requirement.