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A Verifiable Single Keyword Top-k Search Scheme against Insider Attacks over Cloud Data

conference contribution
posted on 2024-11-03, 12:56 authored by Hua Dai, Xiangyang Zhu, Geng Yang, Xun YiXun Yi
With the development of cloud computing and its economic benefit, more and more companies and individuals outsource their data and computation to clouds. Meanwhile, the business way of resource outsourcing makes the data out of control from its owner and results in many security issues. The existing secure keyword search methods assume that cloud servers are curious-but-honest or partial honest, which makes them powerless to deal with the deliberately falsified or fabricated results of insider attacks. In this paper, we propose a verifiable single keyword top-k search scheme against insider attacks which can verify the integrity of search results. Data owners generate verification codes (VCs) for the corresponding files, which embed the ordered sequence information of the relevance scores between files and keywords. Then files and corresponding VCs are outsourced to cloud servers. When a data user performs a keyword search in cloud servers, the qualified result files are determined according to the relevance scores between the files and the interested keyword and then returned to the data user together with a VC. The integrity of the result files is verified by data users through reconstructing a new VC on the received files and comparing it with the received one. Performance evaluation have been conducted to demonstrate the efficiency and result redundancy of the proposed scheme.

History

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  1. 1.
    DOI - Is published in 10.1109/BIGCOM.2017.56
  2. 2.
    ISBN - Is published in 9781538633502 (urn:isbn:9781538633502)

Start page

111

End page

116

Total pages

6

Outlet

Proceedings of the 3rd International Conference on Big Data Computing and Communications (BIGCOM 2017)

Name of conference

BIGCOM 2017

Publisher

IEEE

Place published

United States

Start date

2017-08-10

End date

2017-08-11

Language

English

Copyright

© 2017 IEEE

Former Identifier

2006095905

Esploro creation date

2020-06-22

Fedora creation date

2019-12-18

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