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Ruoyang Guo
Renmin University of China
Range query (data structures)Nearest neighbor searchGeohashComputer scienceCloud computing
2Publications
1H-index
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#1Yuncheng Wu (RUC: Renmin University of China)H-Index: 2
#2Ke Wang (SFU: Simon Fraser University)H-Index: 52
Last. Cuiping Li (RUC: Renmin University of China)H-Index: 6
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Group k-nearest neighbor ( kGNN) search allows a group of nmobile users to jointly retrieve kpoints from a location-based service provider (LSP) that minimizes the aggregate distance to them. We identify four protection objectives in the privacy preserving kGNN search: (i) every user's location should be protected from LSP; (ii) the group's query and the query answer should be protected from LSP; (iii) LSP's private database information should be protected from users; (iv) every u...
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#1Ruoyang Guo (RUC: Renmin University of China)H-Index: 1
#2Bo Qin (RUC: Renmin University of China)H-Index: 1
Last. Cuiping Li (RUC: Renmin University of China)H-Index: 6
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As the location-based applications are flourishing, we will witness soon a prodigious amount of spatial data will be stored in the public cloud with the geometric range query as one of the most fundamental search functions. The rising demand of outsourced data is moving larger-scale datasets and wider-scope query size. To protect the confidentiality of the geographic information of individuals, the outsourced data stored at the cloud server should be preserved especially when they are queried. W...
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