Attribute-Based Encryption (ABE) is an advanced public-key encryption paradigm that enforces fine-grained access control over encrypted data. For instance, the data owner can generate the ciphertext associated with ((”Cardiology” OR ”Critical Care Medicine”) AND ”Consultant Physician”), which can only be decrypted by other data users whose attributes satisfy this access policy. However, a major efficiency drawback of ABE is that ciphertext size, encryption time, and decryption time grow with the complexity of the access policy and the number of attributes. To address this issue, we propose an adaptively secure, offline/online and outsourcing ABE scheme under the Generic Group Model. The majority of encryption operation is performed during the offline phase, while ABE ciphertext is quickly assembled during the online phase. Decryption outsourcing enables the edge node to convert the original ABE ciphertext into an ElGamal-style ciphertext using a transformation key without revealing the plaintext message to the edge node. Our approach is efficient and suitable for mobile devices. Performance evaluation demonstrates the system can balance computation and power consumption over an extended period, effectively addressing challenges in data sharing.

DDoS (Distributed Denial of Service) attacks are increasingly becoming a major threat in the field of cybersecurity. They overwhelm target servers by sending large-scale requests from multiple locations, causing the servers to become unresponsive. The distributed nature of DDoS attacks also makes detection and defense even more challenging. As the damage caused by such attacks grows, the development of efficient detection and mitigation mechanisms has become an urgent priority. While traditional machine learning methods are useful, they still require manual feature extraction, which not only involves significant human intervention, but also is time-consuming. In contrast, deep learning offers an automated approach to feature extraction and can learn more abstract patterns, which leads to improved detection performance. Therefore, this paper reviews and analyzes existing deep learning methods for DDoS attack detection. We provide a comprehensive analysis of the various types of DDoS attacks and explore different deep learning models employed for attack detection. Additionally, we explore techniques such as federated learning that can be integrated with deep learning, and analyze their related literature in DDoS attack detection. Finally, we specify future research directions on DDoS attack detection using deep learning.

Privacy-preserving data evaluation is one of the prominent research topics in the Big Data era. In many data evaluation applications that involve sensitive information, such as the medical records of patients in a medical system, protecting data privacy during the data evaluation process has become an essential requirement. Aiming at solving this problem, numerous fuzzy encryption systems for different similarity metrics have been proposed in literature. Unfortunately, the existing fuzzy encryption systems either fail to achieve attribute-hiding or achieve it, but are impractical. In this article, we propose a new fuzzy encryption scheme for privacy-preserving data evaluation based on overlap distance, which can work in an integer domain while achieving attribute-hiding. In particular, we develop a novel approach to enable an accurate overlap distance to be fast calculated. This technique makes the number of pairing operations during decryption stage negative correlation with the size of the threshold, which is pretty practical for some applications especially with a large threshold. Additionally, we provide a formal security analysis of the proposed scheme, followed by a comprehensive experimental. Also we show that our scheme can be well applied to some scenarios, such as fuzzy keyword searchable encryption and attribute-hiding closest substring encryption.