KEYWORDS: Social networks, Data modeling, Expectation maximization algorithms, Matrices, Data privacy, Connectors, Social network analysis, Semantics, Reflection, Internet
In the area of social network, different attributes have different effects on the structure of network. Most of the existing privacy protection methods for attributed networks ignore the situation which different attributes have different effects on the network structure. They protect the privacy of the attributes indiscriminately. In respect of the issues above, a differentially private discrete multi-attributed network releasing method is proposed. Firstly, a probability model of discrete multi-attributed network is structured and the correlation parameter between multiple attributes and network structure is defined. The factor with different effects of different attributes on network structure is added into the model. Then, the algorithm uses the correlation parameter to establish the partition model of metadata and divides the metadata into different groups. As the group has different network model and attribute between each other, the groups are independence. The differential privacy of discrete multi-attributed network is realized through sanitizing parameters of the model and allocating metadata using exponential mechanism. Finally, experiment on real datasets verifies that the algorithm can satisfy the characteristics of the discrete multi-attributed network. It can also improve the efficiency and data availability.
Semantic segmentation is widely used in remote sensing data extraction and classification. Existing semantic segmentation networks focus on capturing contextual information in many different ways, simply fusing features at different levels, and ultimately improving the accuracy of semantic segmentation. However, low-level semantic features lack spatial context guidance, and high-level semantic features tend to encode large objects with coarse spatial details, making segmentation results prone to losing fine details. In this paper, we analyze the advantages and disadvantages of different levels of feature maps, and enhance the feature representation from two aspects to solve this problem. On the one hand, inspired by the architectural idea of atrous spatial pyramic pooling (ASPP), we adjust the structure of ASPP module and add the attention module to ASPP, and a new Attention-ASPP(AASPP) module is constructed in this paper. On the other hand, feature information such as boundary contours is enhanced by channel attention modeling, thereby improving local detail representation. Comprehensive experimental results show that our model framework achieves excellent segmentation performance on two public datasets, WHU building dataset and ISPRS Potsdam dataset.
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