Practical Attribute-Based Multi-Keyword Search Scheme in Mobile Crowdsourcing

Cloud-based mobile crowd-sourcing has been an attractive solution to provide data storage and share services for resource-limited mobile devices in a privacy-preserving manner, but how to enable mobile users to issue search queries and achieve fine-grained access control over ciphertexts simultaneou...

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Bibliographic Details
Published in:IEEE internet of things journal Vol. 5; no. 4; pp. 3008 - 3018
Main Authors: Miao, Yinbin, Ma, Jianfeng, Liu, Ximeng, Li, Xinghua, Liu, Zhiquan, Li, Hui
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.08.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
0-encoding and 1-encoding
Access control
Algorithms
attribute-based keyword search (ABKS)
chosen-keyword attack
Cloud computing
Coding
Crowdsourcing
Cryptography
Data storage
Electronic devices
Encryption
Internet of Things
Keyword search
Keywords
Mobile communication
mobile crowd-sourcing
Searching
ISSN:2327-4662, 2327-4662
Online Access:Get full text
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Summary:Cloud-based mobile crowd-sourcing has been an attractive solution to provide data storage and share services for resource-limited mobile devices in a privacy-preserving manner, but how to enable mobile users to issue search queries and achieve fine-grained access control over ciphertexts simultaneously is still a big challenge for various circumstances. Although the ciphertext-policy attribute-based keyword search technology combining attribute-based encryption with searchable encryption has become a hot research topic, it just deals with equivalent attributes rather than more practical attribute comparisons, like "greater than" or "less than." In this paper, we devise a practical cryptographic primitive called attribute-based multi-keyword search scheme to support comparable attributes through utilizing 0-encoding and 1-encoding. Formal security analysis proves that our scheme is selectively secure against chosen-keyword attack in generic bilinear group model and extensive experiments using real-world dataset demonstrate that our scheme can drastically decrease both computational and storage costs.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2017.2779124