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A Fast Physical Layer Security-Based Location Privacy Parameter Recommendation Algorithm in 5G IoT
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作者 Hua Zhao Mingyan Xu +1 位作者 Zhou Zhong Ding Wang 《China Communications》 SCIE CSCD 2021年第8期75-84,共10页
The 5G IoT(Internet of Things,IoT)is easier to implement in location privacy-preserving research.The terminals in distributed network architecture blur their accurate locations into a spatial cloaking region but most ... The 5G IoT(Internet of Things,IoT)is easier to implement in location privacy-preserving research.The terminals in distributed network architecture blur their accurate locations into a spatial cloaking region but most existing spatial cloaking algorithms cannot work well because of man-in-the-middle attacks,high communication overhead,time consumption,and the lower success rate.This paper proposes an algorithm that can recommend terminal’s privacy requirements based on getting terminal distribution information in the neighborhood after cross-layer authentication and therefore help 5G IoT terminals find enough collaborative terminals safely and quickly.The approach shows it can avoid man-in-the-middle attacks and needs lower communication costs and less searching time than 520ms at the same time.It has a great anonymization success rate by 93%through extensive simulation experiments for a range of 5G IoT scenarios. 展开更多
关键词 cross-layer authentication location privacy parameter recommendation 5G IoT
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Recommending Personalized POIs from Location Based Social Network
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作者 Haiying Che Di Sang Billy Zimba 《Journal of Beijing Institute of Technology》 EI CAS 2018年第1期137-145,共9页
Location based social networks( LBSNs) provide location specific data generated from smart phone into online social networks thus people can share their points of interest( POIs). POI collections are complex and c... Location based social networks( LBSNs) provide location specific data generated from smart phone into online social networks thus people can share their points of interest( POIs). POI collections are complex and can be influenced by various factors,such as user preferences,social relationships and geographical influence. Therefore,recommending new locations in LBSNs requires to take all these factors into consideration. However,one problem is how to determine optimal weights of influencing factors in an algorithm in which these factors are combined. The user similarity can be obtained from the user check-in data,or from the user friend information,or based on the different geographical influences on each user's check-in activities. In this paper,we propose an algorithm that calculates the user similarity based on check-in records and social relationships,using a proposed weighting function to adjust the weights of these two kinds of similarities based on the geographical distance between users. In addition,a non-parametric density estimation method is applied to predict the unique geographical influence on each user by getting the density probability plot of the distance between every pair of user's check-in locations. Experimental results,using foursquare datasets,have shown that comparisons between the proposed algorithm and the other five baseline recommendation algorithms in LBSNs demonstrate that our proposed algorithm is superior in accuracy and recall,furthermore solving the sparsity problem. 展开更多
关键词 location based social network personalized geographical influence location recommendation non-parametric probability estimates
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