With the advent of the big data era, the size and complexity of data continue to increase, which makes the requirement for data privacy and security increasingly urgent. However, traditional encryption methods cannot meet the demand for efficient searching in large-scale datasets. To solve this problem and enable users to search within encryped data and without decrypting the entire dataset, trapdoor functions and other cryptograhic techniques are introduced in searchable encryption. However, searchable encryption still cannot meet the needs in the real world. Therefore, researchers have introduced the concept of attribute-based encryption into searchable encryption, resulting in attribute-based searchable encryption (ABSE). This approach aims to achieve efficient search by attributes in encrypted datasets. ABSE has a wide range of applications in the fields of privacy protection, data sharing, and cloud computing. In this paper, we describe the trends in development, focusing on enhancing security, improving computational efficiency, and increasing flexibility. We also present the related schemes. In addition, several common application areas are introduced and the relevant schemes proposed by researchers are summarized. Moreover, the challenges and future directions of ABSE are discussed in this paper.

Multivariate time series anomaly detection is crucial for ensuring reliability in critical applications such as Industrial Internet of Things (IIoT) equipment monitoring and cloud computing platform failure early warning. However, existing methods often struggle when dealing with complex time series characterized by high dimensionality, strong inter-dependencies, and significant variability, particularly in regions with concentrated anomalies. To address these challenges, we propose FIDiff, a diffusion model based on fusion and inference. The framework designs a diffusion model tailored for masked data, which incorporates a time series filling denoiser to enhance the modeling of complex temporal dependencies and mitigate interference from anomalies and noise. Furthermore, a unique multi-step diffusion-based fusion and inference strategy is introduced, which integrates intermediate outputs from models with different diffusion steps and employs a dynamic thresholding mechanism for anomaly scoring, thereby significantly improving detection accuracy and stability. Extensive experiments on six public datasets demonstrate that FIDiff achieves an average improvement of 3.7% in F1-score compared with the best-performing model among 13 state-of-the-art baselines, with consistent performance gains across all datasets, validating its effectiveness and practical value. The code for this project is available at https://github.com/secMTS/FIDiff.git.