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A secure and efficient data sharing framework with delegated capabilities in hybrid cloud

conference contribution
posted on 2016-01-04, 00:00 authored by X Liu, Y Xia, Yang Xiang, M M Hassan, A Alelaiwi
Hybrid cloud is a widely used cloud architecture in large companies that can outsource data to the publiccloud, while still supporting various clients like mobile devices. However, such public cloud data outsourcing raises serious security concerns, such as how to preserve data confidentiality and how to regulate access policies to the data stored in public cloud. To address this issue, we design a hybrid cloud architecture that supports data sharing securely and efficiently, even with resource-limited devices, where private cloud serves as a gateway between the public cloud and the data user. Under such architecture, we propose an improved construction of attribute-based encryption that has the capability of delegating encryption/decryption computation, which achieves flexible access control in the cloud and privacy-preserving in datautilization even with mobile devices. Extensive experiments show the scheme can further decrease the computational cost and space overhead at the user side, which is quite efficient for the user with limited mobile devices. In the process of delegating most of the encryption/decryption computation to private cloud, the user can not disclose any information to the private cloud. We also consider the communication securitythat once frequent attribute revocation happens, our scheme is able to resist some attacks between private cloud and data user by employing anonymous key agreement.



Security and Privacy in Social Networks and Big Data. Symposium (2015 : Hangzhou, China)


7 - 14




Hangzhou, China

Place of publication

Piscataway, N.J.

Start date


End date






Publication classification

E Conference publication; E1 Full written paper - refereed

Copyright notice

2015, IEEE



Title of proceedings

SocialSec 2015 : Proceedings of the Security and Privacy in Social Networks and Big Data 2015 Symposium