Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/157625
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dc.contributor.authorZhangjie Fuen_US
dc.contributor.authorFengxiao Huangen_US
dc.contributor.authorKui Renen_US
dc.contributor.authorJian Wengen_US
dc.contributor.authorCong Wangen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T07:18:38Z-
dc.date.available2020-04-06T07:18:38Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TIFS.2017.2692728en_US
dc.identifier.urihttp://localhost/handle/Hannan/157625-
dc.description.abstractSearchable encryption is an important research area in cloud computing. However, most existing efficient and reliable ciphertext search schemes are based on keywords or shallow semantic parsing, which are not smart enough to meet with users&x2019; search intention. Therefore, in this paper, we propose a content-aware search scheme, which can make semantic search more smart. First, we introduce conceptual graphs (CGs) as a knowledge representation tool. Then, we present our two schemes (PRSCG and PRSCG-TF) based on CGs according to different scenarios. In order to conduct numerical calculation, we transfer original CGs into their linear form with some modification and map them to numerical vectors. Second, we employ the technology of multi-keyword ranked search over encrypted cloud data as the basis against two threat models and raise PRSCG and PRSCG-TF to resolve the problem of privacy-preserving smart semantic search based on CGs. Finally, we choose a real-world data set: CNN data set to test our scheme. We also analyze the privacy and efficiency of proposed schemes in detail. The experiment results show that our proposed schemes are efficient.en_US
dc.format.extent1874,en_US
dc.format.extent1884en_US
dc.publisherIEEEen_US
dc.relation.haspart7895156.pdfen_US
dc.titlePrivacy-Preserving Smart Semantic Search Based on Conceptual Graphs Over Encrypted Outsourced Dataen_US
dc.typeArticleen_US
dc.journal.volume12en_US
dc.journal.issue8en_US
Appears in Collections:2017

Files in This Item:
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7895156.pdf1.23 MBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorZhangjie Fuen_US
dc.contributor.authorFengxiao Huangen_US
dc.contributor.authorKui Renen_US
dc.contributor.authorJian Wengen_US
dc.contributor.authorCong Wangen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T07:18:38Z-
dc.date.available2020-04-06T07:18:38Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TIFS.2017.2692728en_US
dc.identifier.urihttp://localhost/handle/Hannan/157625-
dc.description.abstractSearchable encryption is an important research area in cloud computing. However, most existing efficient and reliable ciphertext search schemes are based on keywords or shallow semantic parsing, which are not smart enough to meet with users&x2019; search intention. Therefore, in this paper, we propose a content-aware search scheme, which can make semantic search more smart. First, we introduce conceptual graphs (CGs) as a knowledge representation tool. Then, we present our two schemes (PRSCG and PRSCG-TF) based on CGs according to different scenarios. In order to conduct numerical calculation, we transfer original CGs into their linear form with some modification and map them to numerical vectors. Second, we employ the technology of multi-keyword ranked search over encrypted cloud data as the basis against two threat models and raise PRSCG and PRSCG-TF to resolve the problem of privacy-preserving smart semantic search based on CGs. Finally, we choose a real-world data set: CNN data set to test our scheme. We also analyze the privacy and efficiency of proposed schemes in detail. The experiment results show that our proposed schemes are efficient.en_US
dc.format.extent1874,en_US
dc.format.extent1884en_US
dc.publisherIEEEen_US
dc.relation.haspart7895156.pdfen_US
dc.titlePrivacy-Preserving Smart Semantic Search Based on Conceptual Graphs Over Encrypted Outsourced Dataen_US
dc.typeArticleen_US
dc.journal.volume12en_US
dc.journal.issue8en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7895156.pdf1.23 MBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorZhangjie Fuen_US
dc.contributor.authorFengxiao Huangen_US
dc.contributor.authorKui Renen_US
dc.contributor.authorJian Wengen_US
dc.contributor.authorCong Wangen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T07:18:38Z-
dc.date.available2020-04-06T07:18:38Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TIFS.2017.2692728en_US
dc.identifier.urihttp://localhost/handle/Hannan/157625-
dc.description.abstractSearchable encryption is an important research area in cloud computing. However, most existing efficient and reliable ciphertext search schemes are based on keywords or shallow semantic parsing, which are not smart enough to meet with users&x2019; search intention. Therefore, in this paper, we propose a content-aware search scheme, which can make semantic search more smart. First, we introduce conceptual graphs (CGs) as a knowledge representation tool. Then, we present our two schemes (PRSCG and PRSCG-TF) based on CGs according to different scenarios. In order to conduct numerical calculation, we transfer original CGs into their linear form with some modification and map them to numerical vectors. Second, we employ the technology of multi-keyword ranked search over encrypted cloud data as the basis against two threat models and raise PRSCG and PRSCG-TF to resolve the problem of privacy-preserving smart semantic search based on CGs. Finally, we choose a real-world data set: CNN data set to test our scheme. We also analyze the privacy and efficiency of proposed schemes in detail. The experiment results show that our proposed schemes are efficient.en_US
dc.format.extent1874,en_US
dc.format.extent1884en_US
dc.publisherIEEEen_US
dc.relation.haspart7895156.pdfen_US
dc.titlePrivacy-Preserving Smart Semantic Search Based on Conceptual Graphs Over Encrypted Outsourced Dataen_US
dc.typeArticleen_US
dc.journal.volume12en_US
dc.journal.issue8en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7895156.pdf1.23 MBAdobe PDF