Xudong Yang, Ling Gao, Hai Wang, Yan Li, Jie Zheng, Jipeng Xu, Yuhui Ma
{"title":"基于位置的服务中基于用户的语义位置隐私保护方法","authors":"Xudong Yang, Ling Gao, Hai Wang, Yan Li, Jie Zheng, Jipeng Xu, Yuhui Ma","doi":"10.1109/ICPADS53394.2021.00092","DOIUrl":null,"url":null,"abstract":"With the popularity and development of Location-Based Services (LBS), location privacy-preservation has become a hot research topic in recent years, especially research on k-anonymity. Although previous studies have done a lot of work on privacy protection, they ignore the negative impact on the security of the knowledge of user-related semantic information of locations that attacker has. To solve this issue, we proposed a User-related Semantic Location Privacy Protection Mechanism (USPPM) based on k-anonymity. First, the anonymity set generation method that combines user-related mobile semantic feature of locations and semantic diversity entropy is proposed to improve the location semantic privacy safety. Second, we design an anonymity set optimization method which enhances sensitive semantic location privacy, through stackberg game model between attacker and protector. Finally, compared with other solutions, experiment on the real dataset shows that our algorithms can provide location privacy efficiently.","PeriodicalId":309508,"journal":{"name":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A User-related Semantic Location Privacy Protection Method In Location-based Service\",\"authors\":\"Xudong Yang, Ling Gao, Hai Wang, Yan Li, Jie Zheng, Jipeng Xu, Yuhui Ma\",\"doi\":\"10.1109/ICPADS53394.2021.00092\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the popularity and development of Location-Based Services (LBS), location privacy-preservation has become a hot research topic in recent years, especially research on k-anonymity. Although previous studies have done a lot of work on privacy protection, they ignore the negative impact on the security of the knowledge of user-related semantic information of locations that attacker has. To solve this issue, we proposed a User-related Semantic Location Privacy Protection Mechanism (USPPM) based on k-anonymity. First, the anonymity set generation method that combines user-related mobile semantic feature of locations and semantic diversity entropy is proposed to improve the location semantic privacy safety. Second, we design an anonymity set optimization method which enhances sensitive semantic location privacy, through stackberg game model between attacker and protector. Finally, compared with other solutions, experiment on the real dataset shows that our algorithms can provide location privacy efficiently.\",\"PeriodicalId\":309508,\"journal\":{\"name\":\"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPADS53394.2021.00092\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPADS53394.2021.00092","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A User-related Semantic Location Privacy Protection Method In Location-based Service
With the popularity and development of Location-Based Services (LBS), location privacy-preservation has become a hot research topic in recent years, especially research on k-anonymity. Although previous studies have done a lot of work on privacy protection, they ignore the negative impact on the security of the knowledge of user-related semantic information of locations that attacker has. To solve this issue, we proposed a User-related Semantic Location Privacy Protection Mechanism (USPPM) based on k-anonymity. First, the anonymity set generation method that combines user-related mobile semantic feature of locations and semantic diversity entropy is proposed to improve the location semantic privacy safety. Second, we design an anonymity set optimization method which enhances sensitive semantic location privacy, through stackberg game model between attacker and protector. Finally, compared with other solutions, experiment on the real dataset shows that our algorithms can provide location privacy efficiently.