Secure semantic search using deep learning in a blockchain-assisted multi-user setting
Abstract Deep learning-based semantic search (DLSS) aims to bridge the gap between experts and non-experts in search. Experts can create precise queries due to their prior knowledge, while non-experts struggle with specific terms and concepts, making their queries less precise. Cloud infrastructure...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-01-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13677-023-00578-5 |
| _version_ | 1797273215449432064 |
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| author | Shahzad Khan Haider Abbas Muhammad Binsawad |
| author_facet | Shahzad Khan Haider Abbas Muhammad Binsawad |
| author_sort | Shahzad Khan |
| collection | DOAJ |
| description | Abstract Deep learning-based semantic search (DLSS) aims to bridge the gap between experts and non-experts in search. Experts can create precise queries due to their prior knowledge, while non-experts struggle with specific terms and concepts, making their queries less precise. Cloud infrastructure offers a practical and scalable platform for data owners to upload their data, making it accessible to intended data users. However, the contemporary single-owner/single-user (S/S) approach to DLSS schemes falls short of effectively leveraging the inherent multi-user capabilities of cloud infrastructure. Furthermore, most of these schemes delegate the dissemination of secret keys to a single trust point within the mutual distrust scenario in cloud infrastructure. This paper proposes a Secure Semantic Search using Deep Learning in a Blockchain-Assisted Multi-User Setting $$(S^3DBMS)$$ ( S 3 D B M S ) . Specifically, the seamless integration of attribute-based encryption with transfer learning allows the construction of DLSS in multi-owner/multi-user (M/M) settings. Further, blockchain’s smart contract mechanism allows a multi-attribute authority consensus-based generation of user private keys and system-wide global parameters in a mutual distrust M/M scenario. Finally, our scheme achieves privacy requirements and offers improved security and accuracy. |
| first_indexed | 2024-03-07T14:40:23Z |
| format | Article |
| id | doaj.art-05cb841971f54ffc95315a1049686a2a |
| institution | Directory Open Access Journal |
| issn | 2192-113X |
| language | English |
| last_indexed | 2024-03-07T14:40:23Z |
| publishDate | 2024-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj.art-05cb841971f54ffc95315a1049686a2a2024-03-05T20:22:18ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2024-01-0113111910.1186/s13677-023-00578-5Secure semantic search using deep learning in a blockchain-assisted multi-user settingShahzad Khan0Haider Abbas1Muhammad Binsawad2University of Science and Technology, NUSTUniversity of Science and Technology, NUSTKing Abdulaziz UniversityAbstract Deep learning-based semantic search (DLSS) aims to bridge the gap between experts and non-experts in search. Experts can create precise queries due to their prior knowledge, while non-experts struggle with specific terms and concepts, making their queries less precise. Cloud infrastructure offers a practical and scalable platform for data owners to upload their data, making it accessible to intended data users. However, the contemporary single-owner/single-user (S/S) approach to DLSS schemes falls short of effectively leveraging the inherent multi-user capabilities of cloud infrastructure. Furthermore, most of these schemes delegate the dissemination of secret keys to a single trust point within the mutual distrust scenario in cloud infrastructure. This paper proposes a Secure Semantic Search using Deep Learning in a Blockchain-Assisted Multi-User Setting $$(S^3DBMS)$$ ( S 3 D B M S ) . Specifically, the seamless integration of attribute-based encryption with transfer learning allows the construction of DLSS in multi-owner/multi-user (M/M) settings. Further, blockchain’s smart contract mechanism allows a multi-attribute authority consensus-based generation of user private keys and system-wide global parameters in a mutual distrust M/M scenario. Finally, our scheme achieves privacy requirements and offers improved security and accuracy.https://doi.org/10.1186/s13677-023-00578-5Blockchain-based verificationSemantic searchMulti-authority attribute-based encryptionSecure transfer learningAttribute based access control |
| spellingShingle | Shahzad Khan Haider Abbas Muhammad Binsawad Secure semantic search using deep learning in a blockchain-assisted multi-user setting Journal of Cloud Computing: Advances, Systems and Applications Blockchain-based verification Semantic search Multi-authority attribute-based encryption Secure transfer learning Attribute based access control |
| title | Secure semantic search using deep learning in a blockchain-assisted multi-user setting |
| title_full | Secure semantic search using deep learning in a blockchain-assisted multi-user setting |
| title_fullStr | Secure semantic search using deep learning in a blockchain-assisted multi-user setting |
| title_full_unstemmed | Secure semantic search using deep learning in a blockchain-assisted multi-user setting |
| title_short | Secure semantic search using deep learning in a blockchain-assisted multi-user setting |
| title_sort | secure semantic search using deep learning in a blockchain assisted multi user setting |
| topic | Blockchain-based verification Semantic search Multi-authority attribute-based encryption Secure transfer learning Attribute based access control |
| url | https://doi.org/10.1186/s13677-023-00578-5 |
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