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Query-Biased Preview over Outsourced and Encrypted Data
For both convenience and security, more and more users encrypt their sensitive data before outsourcing it to a third party such as cloud storage service. However, searching for the desired documents becomes problematic since it is costly to download and decrypt each possibly needed document to check...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3775409/ https://www.ncbi.nlm.nih.gov/pubmed/24078798 http://dx.doi.org/10.1155/2013/860621 |
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author | Peng, Ningduo Luo, Guangchun Qin, Ke Chen, Aiguo |
author_facet | Peng, Ningduo Luo, Guangchun Qin, Ke Chen, Aiguo |
author_sort | Peng, Ningduo |
collection | PubMed |
description | For both convenience and security, more and more users encrypt their sensitive data before outsourcing it to a third party such as cloud storage service. However, searching for the desired documents becomes problematic since it is costly to download and decrypt each possibly needed document to check if it contains the desired content. An informative query-biased preview feature, as applied in modern search engine, could help the users to learn about the content without downloading the entire document. However, when the data are encrypted, securely extracting a keyword-in-context snippet from the data as a preview becomes a challenge. Based on private information retrieval protocol and the core concept of searchable encryption, we propose a single-server and two-round solution to securely obtain a query-biased snippet over the encrypted data from the server. We achieve this novel result by making a document (plaintext) previewable under any cryptosystem and constructing a secure index to support dynamic computation for a best matched snippet when queried by some keywords. For each document, the scheme has O(d) storage complexity and O(log(d/s) + s + d/s) communication complexity, where d is the document size and s is the snippet length. |
format | Online Article Text |
id | pubmed-3775409 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-37754092013-09-29 Query-Biased Preview over Outsourced and Encrypted Data Peng, Ningduo Luo, Guangchun Qin, Ke Chen, Aiguo ScientificWorldJournal Research Article For both convenience and security, more and more users encrypt their sensitive data before outsourcing it to a third party such as cloud storage service. However, searching for the desired documents becomes problematic since it is costly to download and decrypt each possibly needed document to check if it contains the desired content. An informative query-biased preview feature, as applied in modern search engine, could help the users to learn about the content without downloading the entire document. However, when the data are encrypted, securely extracting a keyword-in-context snippet from the data as a preview becomes a challenge. Based on private information retrieval protocol and the core concept of searchable encryption, we propose a single-server and two-round solution to securely obtain a query-biased snippet over the encrypted data from the server. We achieve this novel result by making a document (plaintext) previewable under any cryptosystem and constructing a secure index to support dynamic computation for a best matched snippet when queried by some keywords. For each document, the scheme has O(d) storage complexity and O(log(d/s) + s + d/s) communication complexity, where d is the document size and s is the snippet length. Hindawi Publishing Corporation 2013-09-02 /pmc/articles/PMC3775409/ /pubmed/24078798 http://dx.doi.org/10.1155/2013/860621 Text en Copyright © 2013 Ningduo Peng et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Peng, Ningduo Luo, Guangchun Qin, Ke Chen, Aiguo Query-Biased Preview over Outsourced and Encrypted Data |
title | Query-Biased Preview over Outsourced and Encrypted Data |
title_full | Query-Biased Preview over Outsourced and Encrypted Data |
title_fullStr | Query-Biased Preview over Outsourced and Encrypted Data |
title_full_unstemmed | Query-Biased Preview over Outsourced and Encrypted Data |
title_short | Query-Biased Preview over Outsourced and Encrypted Data |
title_sort | query-biased preview over outsourced and encrypted data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3775409/ https://www.ncbi.nlm.nih.gov/pubmed/24078798 http://dx.doi.org/10.1155/2013/860621 |
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