A spatial entropy reflecting distribution of spatial objects

Decision trees are mainly used to classify data and predict data classes. A spatial decision tree has been designed using Euclidean distance between objects for reflecting spatial data characteristic. Even though this method explains the distance of objects in spatial dimension, it fails to represent distributions of spatial data and their relationships. But distributions of spatial data and relationships with their neighborhoods are very important in real world. This paper proposes decision tree based on spatial entropy that represents distributions of spatial data with dispersion and dissimilarity. The rate of dispersion by dissimilarity presents how related distribution of spatial data and non-spatial attributes. The experiment evaluates the accuracy and building time of decision tree as compared to previous methods and it shows that the proposed method makes efficient and scalable classification for spatial decision support.

Neighborhood-Based In-Network Caching for Information-Centric Networks

The current Internet is based on host-centric networking, and a user needs to know the host address before reaching a data target in the network. The new architecture of information-centric networking (ICN) facilitates users to locate data targets by giving their data names without any information about host addresses. In-network caching is one of the prominent features in ICN, which allows network routers to cache data contents. In this paper, we emphasize the management of in-network cache storage, and this includes the mechanisms of cache replacement and cache replication. A new cost function is then proposed to evaluate each cache content and the least valuable content is evicted when cache is full. To increase cache utilization, a cooperative caching policy among neighboring routers is proposed. The proper network locations to cache data contents are also discussed in the paper. Experimental results show the superiority of the proposed caching policy than some traditional caching polices.

Privacy-Preserving Deep Learning on Big Data in Cloud

In the analysis of big data,deep learn-ing is a crucial technique.Big data analysis tasks are typically carried out on the cloud since it offers strong computer capabilities and storage areas.Nev-ertheless,there is a contradiction between the open nature of the cloud and the demand that data own-ers maintain their privacy.To use cloud resources for privacy-preserving data training,a viable method must be found.A privacy-preserving deep learning model(PPDLM)is suggested in this research to ad-dress this preserving issue.To preserve data privacy,we first encrypted the data using homomorphic en-cryption(HE)approach.Moreover,the deep learn-ing algorithm’s activation function—the sigmoid func-tion—uses the least-squares method to process non-addition and non-multiplication operations that are not allowed by homomorphic.Finally,experimental re-sults show that PPDLM has a significant effect on the protection of data privacy information.Compared with Non-Privacy Preserving Deep Learning Model(NPPDLM),PPDLM has higher computational effi-ciency.