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Obtain confidentiality or/and authenticity in Big Data by ID-based generalized signcryption

Wei, Guiyi, Shao, Jun, Xiang, Yang, Zhu, Pingping and Lu, Rongxing 2015, Obtain confidentiality or/and authenticity in Big Data by ID-based generalized signcryption, Information sciences, vol. 318, pp. 111-122, doi: 10.1016/j.ins.2014.05.034.

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Title Obtain confidentiality or/and authenticity in Big Data by ID-based generalized signcryption
Author(s) Wei, Guiyi
Shao, Jun
Xiang, YangORCID iD for Xiang, Yang orcid.org/0000-0001-5252-0831
Zhu, Pingping
Lu, Rongxing
Journal name Information sciences
Volume number 318
Start page 111
End page 122
Total pages 12
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-10-10
ISSN 0020-0255
Keyword(s) Authenticity
Big Data
Confidentiality
Generalized signcryption
Provable security
Standard model
Summary Recently, the Big Data paradigm has received considerable attention since it gives a great opportunity to mine knowledge from massive amounts of data. However, the new mined knowledge will be useless if data is fake, or sometimes the massive amounts of data cannot be collected due to the worry on the abuse of data. This situation asks for new security solutions. On the other hand, the biggest feature of Big Data is "massive", which requires that any security solution for Big Data should be "efficient". In this paper, we propose a new identity-based generalized signcryption scheme to solve the above problems. In particular, it has the following two properties to fit the efficiency requirement. (1) It can work as an encryption scheme, a signature scheme or a signcryption scheme as per need. (2) It does not have the heavy burden on the complicated certificate management as the traditional cryptographic schemes. Furthermore, our proposed scheme can be proven-secure in the standard model. © 2014 Elsevier Inc. All rights reserved.
Language eng
DOI 10.1016/j.ins.2014.05.034
Field of Research 080303 Computer System Security
Socio Economic Objective 890202 Application Tools and System Utilities
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30072048

Document type: Journal Article
Collection: School of Information Technology
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Citation counts: TR Web of Science Citation Count  Cited 10 times in TR Web of Science
Scopus Citation Count Cited 14 times in Scopus
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Created: Thu, 02 Apr 2015, 16:14:45 EST

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