Due to high capacity and fast transmission speed, 5G plays a key role in modern electronic infrastructure. Meanwhile, sparse tensor factorization (STF) is a useful tool for dimension reduction to analyze high-order, high-dimension, and sparse tensor (HOHDST) data, which is transmitted on 5G Internet-of-things (IoT). Hence, HOHDST data relies on STF to obtain complete data and discover rules for real time and accurate analysis. From another view of computation and data security, the current STF solution seeks to improve the computational efficiency but neglects privacy security of the IoT data, e.g., data analysis for network traffic monitor system. To overcome these problems, this article proposes a multiple-strategies differential privacy framework on STF (MDPSTF) for HOHDST network traffic data analysis. MDPSTF comprises three differential privacy (DP) mechanisms, i.e., varepsilon - DP, concentrated DP, and local DP. Furthermore, the theoretical proof of privacy bound is presented. Hence, MDPSTF can provide general data protection for HOHDST network traffic data with high-security promise. We conduct experiments on two real network traffic datasets (Abilene and Ggrave{E}ANT). The experimental results show that MDPSTF has high universality on the various degrees of privacy protection demands and high recovery accuracy for the HOHDST network traffic data.

Sparse Tucker Decomposition (STD) algorithms learn a core tensor and a group of factor matrices to obtain an optimal low-rank representation feature for the High-Order, High-Dimension, and Sparse Tensor (HOHDST). However, existing STD algorithms face the problem of intermediate variables explosion which results from the fact that the formation of those variables, i.e., matrices Khatri-Rao product, Kronecker product, and matrix-matrix multiplication, follows the whole elements in sparse tensor. The above problems prevent deep fusion of efficient computation and big data platforms. To overcome the bottleneck, a novel stochastic optimization strategy (SGD__Tucker) is proposed for STD which can automatically divide the high-dimension intermediate variables into small batches of intermediate matrices. Specifically, SGD__Tucker only follows the randomly selected small samples rather than the whole elements, while maintaining the overall accuracy and convergence rate. In practice, SGD__Tucker features the two distinct advancements over the state of the art. First, SGD__Tucker can prune the communication overhead for the core tensor in distributed settings. Second, the low data-dependence of SGD__Tucker enables fine-grained parallelization, which makes SGD__Tucker obtaining lower computational overheads with the same accuracy. Experimental results show that SGD__Tucker runs at least 2XX faster than the state of the art.

Plasmon-induced photocatalysts hold great promise for solar energy conversion owing to their strong light-harvesting ability and tunable optical properties. However, the complex process of interfacial extraction of hot carriers and the roles of metal/semiconductor interfaces in plasmonic photocatalysts are still not clearly understood. Herein, the manipulation of the interface between a plasmon metal (Au) and a semiconductor (rutile TiO₂) by introducing an interfacial metal oxide (Al₂O₃) is reported. The resulting Au/Al₂O₃/TiO₂ exhibits remarkable enhancement in photocatalytic water oxidation activity compared with Au/TiO₂, giving an apparent quantum efficiency exceeding 1.3% at 520 nm for photocatalytic water oxidation. Such an interfacial modulation approach significantly prolongs the lifetime of hot carriers in the Au/TiO₂ system, which conclusively improves the utilization of hot carriers for plasmon-induced water oxidation reaction upon irradiation. This work emphasizes the essential role of the interfacial structure in plasmonic devices and provides an alternative method for designing efficient plasmonic photocatalysts for solar energy conversion.