Deep learning has superior performance in defect detection， however， due to the low defect probability， the detection process of defect-free images occupies most of the calculation time， which seriously limits the overall effective detection speed. In order to solve the above problem， a section steel surface defect detection algorithm based on cascade network named SDNet （Select and Detect Network） was proposed. The proposed algorithm was divided into two stages： the pre-inspection stage and the precise detection stage. In the pre-inspection stage， the lightweight ResNet pre-inspection network based on Depthwise Separable Convolution （DSC） and multi-scale parallel convolution was used to determine whether there were defects in the surface image of the section steel. In the precise detection stage， the YOLOv3 was used as the baseline network to accurately classify and locate the defects in the image. In addition， the improved Atrous Spatial Pyramid Pooling （ASPP） module and dual attention module were introduced in the backbone feature extraction network and prediction branches to improve the network detection performance. Experimental results show that the detection speed and the accuracy of SDNet on 1 024 pixel×1 024 pixel images reach 120.63 frames per second and 92.1% respectively. Compared to the original YOLOv3 algorithm， the proposed algorithm has the detection speed of about 3.7 times and the detection precision improved by 10.4 percentage points. The proposed algorithm can be applied to the rapid detection of section steel surface defects.

In order to mitigate the security risks in Location Based Service (LBS) with continuous query attacking algorithm, a new algorithm — Continuous Queries Attacking algorithm based on Cellular Ant (CQACA) was proposed by k-anonymity measurement. At first, the objective function of query recognition rate was defined with entropy and anonymity measurement, and the algorithmic process of objective function was presented by cellular ant. Finally, a simulation with the moving object data generator was conducted to study the key factors of CQACA, and the performance between CQACA and Cloaking was compared. Compared with the actual trajectory, the error of CQACA was 13.27%, and error of Cloaking was 17.35%. The result shows that CQACA has better effectiveness.