An efficient privacy – preserving Ranked Multi-Keyword Retrieval.pptx
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| Titel: | An efficient privacy – preserving Ranked Multi-Keyword Retrieval.pptx |
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| Autoren: | Mohan Raj U |
| Publikationsjahr: | 2025 |
| Schlagwörter: | Private policing and security services, Cloud computing, java development services, java code vulnerabilties |
| Beschreibung: | With the increasing adoption of cloud storage by both individuals and organizations, data providers frequently offload their data to the cloud to alleviate memory constraints and enable rapid data retrieval, which has become a growing trend. Ensuring the confidentiality of this data has led to the development of various encrypted cloud storage solutions for ranked multi-keyword searches. However, many existing approaches are vulnerable to keyword guessing attacks, and the ranked top-K search results retrieved from encrypted cloud data are often inaccurate. To address these issues, we propose a new and efficient privacy-preserving ranked multi- keyword retrieval scheme (PRMKR). In PRMKR, data providers can securely transfer their encrypted data and corresponding inverted indexes to the cloud. Registered users can then perform accurate searches without revealing their trapdoor information to the cloud server. Our approach introduces an encrypted searchable plugin server and lower-dimensional inverted index vectors, which enhance both data confidentiality and search efficiency. Security analysis demonstrates that PRMKR successfully resists keyword guessing attacks, while experimental results confirm its strong computational and communication efficiency. By leveraging a secure index structure, homomorphic encryption, and a relevance scoring mechanism, the proposed solution ensures robust data and index privacy, query unlinkability, and resistance to collusion attacks. Experimental evaluations demonstrate that the scheme achieves high efficiency, scalability, and precision in search results. Adoption of cloud storage by both individuals and organizations, data providers frequently offload their data to the cloud to alleviate memory constraints and enable rapid data retrieval, which has become a growing trend. Ensuring the confidentiality of this data has led to the development of various encrypted cloud storage solutions for ranked multi- keyword searches. However, many existing approaches are vulnerable to keyword ... |
| Publikationsart: | article in journal/newspaper |
| Sprache: | unknown |
| DOI: | 10.6084/m9.figshare.29264249.v1 |
| Verfügbarkeit: | https://doi.org/10.6084/m9.figshare.29264249.v1 https://figshare.com/articles/journal_contribution/An_efficient_privacy_preserving_Ranked_Multi-Keyword_Retrieval_pptx/29264249 |
| Rights: | CC BY 4.0 |
| Dokumentencode: | edsbas.1CA28F6 |
| Datenbank: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | With the increasing adoption of cloud storage by both individuals and organizations, data providers frequently offload their data to the cloud to alleviate memory constraints and enable rapid data retrieval, which has become a growing trend. Ensuring the confidentiality of this data has led to the development of various encrypted cloud storage solutions for ranked multi-keyword searches. However, many existing approaches are vulnerable to keyword guessing attacks, and the ranked top-K search results retrieved from encrypted cloud data are often inaccurate. To address these issues, we propose a new and efficient privacy-preserving ranked multi- keyword retrieval scheme (PRMKR). In PRMKR, data providers can securely transfer their encrypted data and corresponding inverted indexes to the cloud. Registered users can then perform accurate searches without revealing their trapdoor information to the cloud server. Our approach introduces an encrypted searchable plugin server and lower-dimensional inverted index vectors, which enhance both data confidentiality and search efficiency. Security analysis demonstrates that PRMKR successfully resists keyword guessing attacks, while experimental results confirm its strong computational and communication efficiency. By leveraging a secure index structure, homomorphic encryption, and a relevance scoring mechanism, the proposed solution ensures robust data and index privacy, query unlinkability, and resistance to collusion attacks. Experimental evaluations demonstrate that the scheme achieves high efficiency, scalability, and precision in search results. Adoption of cloud storage by both individuals and organizations, data providers frequently offload their data to the cloud to alleviate memory constraints and enable rapid data retrieval, which has become a growing trend. Ensuring the confidentiality of this data has led to the development of various encrypted cloud storage solutions for ranked multi- keyword searches. However, many existing approaches are vulnerable to keyword ... |
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| DOI: | 10.6084/m9.figshare.29264249.v1 |