Nowadays, vast and rapidly growing information acts as digital records of social activities and is widely collected and stored as economic assets. To reduce the difficulty and local data management’s cost significantly, cloud storage services provide a highly available, high-performance, and low-cost solution for user data hosting, enabling remote access, backup, and sharing of data stored by the cloud. However, this service model is not without security risks, including user privacy exposure, low trustworthiness of data, and unauthorized access. To address these concerns, attribute-based encryption

In a paper published in SCIENCE CHINA Chemistry, a research team led by Prof. Hao Sun at Shanghai Jiao Tong University discussed the material designs and process optimization of dry-coating electrode fabrication toward sustainable battery production. The review identifies the key challenge of uneven component dispersion in solvent-free systems and introduces emerging strategies designed to effectively address this issue, including the use of composite binders, functional additives, and thermal-assisted processing. These advances enable the fabrication of electrodes that combine structural integrity with enhanced electrochemical performance, while simultaneously reducing manufacturing costs and energy consumption. By elucidating fundamental design principles and presenting practical pathways, this review contributes to the development of more efficient and environmentally sustainable battery technologies.

The security of digital signatures has been a focus of research in emerging area. Threshold signature is a technique designed for distributed scenarios, allowing the signing key to be divided into multiple shares. This approach can resist single point failures of device and ensure key security. However, most existing surveys on threshold signature schemes are either not comprehensive or cover only a limited range of signature types.

Sub-headline: BIT researchers introduce Malcom to tackle cross-domain encrypted traffic detection using self-supervised learning.

 

Deep neural networks (DNNs) are demonstrated to be vulnerable to adversarial examples. Adversarial training is mainstrem method to improve adversarial robustness of DNNs, which augments the training set with adversarial examples and adopts adversarial regularization loss to improve the robustness of DNNs. Existing adversarial training methods are facing the challenge to balance the accuracy and robustness.

Sub-headline: Beihang University researchers introduce a multi-authority ABE scheme, tackling dynamic access control challenges in cloud data.