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Improving Security and Efficiency in Attribute-Based Data Sharing

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1 Author(s)
Junbeom Hur ; Sch. of Comput. Sci. & Eng., Chuang-Ang Univ., Seoul, South Korea

With the recent adoption and diffusion of the data sharing paradigm in distributed systems such as online social networks or cloud computing, there have been increasing demands and concerns for distributed data security. One of the most challenging issues in data sharing systems is the enforcement of access policies and the support of policies updates. Ciphertext policy attribute-based encryption (CP-ABE) is becoming a promising cryptographic solution to this issue. It enables data owners to define their own access policies over user attributes and enforce the policies on the data to be distributed. However, the advantage comes with a major drawback which is known as a key escrow problem. The key generation center could decrypt any messages addressed to specific users by generating their private keys. This is not suitable for data sharing scenarios where the data owner would like to make their private data only accessible to designated users. In addition, applying CP-ABE in the data sharing system introduces another challenge with regard to the user revocation since the access policies are defined only over the attribute universe. Therefore, in this study, we propose a novel CP-ABE scheme for a data sharing system by exploiting the characteristic of the system architecture. The proposed scheme features the following achievements: 1) the key escrow problem could be solved by escrow-free key issuing protocol, which is constructed using the secure two-party computation between the key generation center and the data-storing center, and 2) fine-grained user revocation per each attribute could be done by proxy encryption which takes advantage of the selective attribute group key distribution on top of the ABE. The performance and security analyses indicate that the proposed scheme is efficient to securely manage the data distributed in the data sharing system.

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:25 ,  Issue: 10 )