{"title":"一种基于位置服务的虚拟k -匿名位置隐私保护快速匹配方法","authors":"Xiaohui Zhu, Renlong Qi","doi":"10.6633/IJNS.202109_23(5).16","DOIUrl":null,"url":null,"abstract":"This paper proposes a dummy k-anonymous location privacy protection based on a fast-matching method that adopts the space coordinate transformation algorithm. First, the 2-D coordinates are converted to binary Morton code. With the fast matching method, non-adjacent position points distributed in different grids are selected as candidate sets of dummy positions. Then, the semantic similarity of place name information of position points in the candidate sets is calculated using the edit distance, and k-1 position points with the smallest semantic similarity are selected as dummy positions. While satisfying the semantic l-diversity and physical dispersion, this method can improve the generation efficiency of dummy locations and further improve the quality of location service.","PeriodicalId":93303,"journal":{"name":"International journal of network security & its applications","volume":"30 1","pages":"888-894"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A New Fast Matching Method for Dummy K-anonymous Location Privacy Protection in Location Based Services\",\"authors\":\"Xiaohui Zhu, Renlong Qi\",\"doi\":\"10.6633/IJNS.202109_23(5).16\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a dummy k-anonymous location privacy protection based on a fast-matching method that adopts the space coordinate transformation algorithm. First, the 2-D coordinates are converted to binary Morton code. With the fast matching method, non-adjacent position points distributed in different grids are selected as candidate sets of dummy positions. Then, the semantic similarity of place name information of position points in the candidate sets is calculated using the edit distance, and k-1 position points with the smallest semantic similarity are selected as dummy positions. While satisfying the semantic l-diversity and physical dispersion, this method can improve the generation efficiency of dummy locations and further improve the quality of location service.\",\"PeriodicalId\":93303,\"journal\":{\"name\":\"International journal of network security & its applications\",\"volume\":\"30 1\",\"pages\":\"888-894\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of network security & its applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.6633/IJNS.202109_23(5).16\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of network security & its applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.6633/IJNS.202109_23(5).16","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A New Fast Matching Method for Dummy K-anonymous Location Privacy Protection in Location Based Services
This paper proposes a dummy k-anonymous location privacy protection based on a fast-matching method that adopts the space coordinate transformation algorithm. First, the 2-D coordinates are converted to binary Morton code. With the fast matching method, non-adjacent position points distributed in different grids are selected as candidate sets of dummy positions. Then, the semantic similarity of place name information of position points in the candidate sets is calculated using the edit distance, and k-1 position points with the smallest semantic similarity are selected as dummy positions. While satisfying the semantic l-diversity and physical dispersion, this method can improve the generation efficiency of dummy locations and further improve the quality of location service.