An image partition security-sharing mechanism based on blockchain and chaotic encryption.

To ensure optimal use of images while preserving privacy, it is necessary to partition the shared image into public and private areas, with public areas being openly accessible and private areas being shared in a controlled and privacy-preserving manner. Current works only facilitate image-level sharing and use common cryptographic algorithms. To ensure efficient, controlled, and privacy-preserving image sharing at the area level, this paper proposes an image partition security-sharing mechanism based on blockchain and chaotic encryption, which mainly includes a fine-grained access control method based on Attribute-Based Access Control (ABAC) and an image-specific chaotic encryption scheme.

Component-controlled synthesis of PdSn nanocrystals on carbon nanotubes as advanced electrocatalysts for oxygen reduction reaction.

Pd-based bimetallic or multimetallic nanocrystals are considered to be potential electrocatalysts for cathodic oxygen reduction reaction (ORR) in fuel cells. Although much advance has been made, the synthesis of component-controlled Pd-Sn alloy nanocrystals or corresponding nanohybrids is still challenging, and the electrocatalytic ORR properties are not fully explored. Herein, component-controlled synthesis of PdSn nanocrystals (including PdSn, PdSn, PdSn and PdSn) has been realized, which are grown or deposited on pre-treated multi-walled carbon nanotubes (CNTs) to form well-coupled nanohybrids (NHs) by a facile one-pot non-hydrolytic system thermolysis method.

Adversarial robustness in deep neural networks based on variable attributes of the stochastic ensemble model.

Deep neural networks (DNNs) have been shown to be susceptible to critical vulnerabilities when attacked by adversarial samples. This has prompted the development of attack and defense strategies similar to those used in cyberspace security. The dependence of such strategies on attack and defense mechanisms makes the associated algorithms on both sides appear as closely processes, with the defense method being particularly passive in these processes.

A Weighted GraphSAGE-Based Context-Aware Approach for Big Data Access Control.

Context information is the key element to realizing dynamic access control of big data. However, existing context-aware access control (CAAC) methods do not support automatic context awareness and cannot automatically model and reason about context relationships. To solve these problems, this article proposes a weighted GraphSAGE-based context-aware approach for big data access control.