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 $G\grave{E}ANT$ ). 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.

Aiming at the search of the underwater acoustic beacons used in aircraft or ship parameter recorders, this paper mainly introduces the underwater acoustic beacon detection and location method of the short baseline array system. According to the signal form of underwater acoustic beacon, this paper discusses the basic algorithm and the engineering algorithm applied in this system. In theory, the basic algorithm is a necessary algorithm for short baseline array to calculate target azimuth, which including the two-element linear array direction measurement algorithm, two-direction cross-location algorithm and the virtual direction discrimination algorithm. In addition, according to the experience of offshore operations, this paper introduces the engineering practice methods such as the angle measurement, the data extraction, the location ray screening and the location ray starting point coordinate calculation. The combination of engineering algorithm and basic algorithm can effectively reduce the impact of the test equipment and environment, and improve the accuracy of underwater acoustic beacon detection and position, which has been tested in actual operation at sea.