Title: A new efficient privacy-preserving data publish-subscribe scheme

Authors: Ping Chen; Zhiying Wang; Xiaoling Tao

Addresses: State Key Laboratory of Integrated Service Networks(ISN), Xidian University, Xi'an, Shaanxi, China ' State Key Laboratory of Integrated Service Networks(ISN), Xidian University, Xi'an, Shaanxi, China ' School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, Guangxi, China

Abstract: Data publish-subscribe is an efficient service for users to share and receive data selectively. Due to the powerful computing resources and storage capacity, the cloud platform is considered as the most appropriate choice to publish and subscribe large-scale data generated in real-world life. However, the cloud platform may be curious about the content of published data and subscribers' interests. In this paper, we aimed at realising a secure and efficient privacy-preserving data publish-subscribe scheme on cloud platforms. On one hand, we adopt ciphertext-policy attribute-based encryption (CPABE) to encrypt the data based on it's access policy. Moreover, part of the decryption computation is shifted to the cloud platform to reduce subscribers' computation overhead. On the other hand, we utilise an efficient searchable encryption scheme based on Bloom Filter tree (BFtree) to protect subscribers' privacy and match their interests with encrypted data. Not only that, publishers and subscribers can also exchange their roles in our scheme. The security analysis and experimental results prove that our scheme is efficient and secure in privacy-preserving data publish-subscribe service.

Keywords: privacy-preserving; data publish-subscribe; CP-ABE; BFtree; cloud platform.

DOI: 10.1504/IJES.2019.099406

International Journal of Embedded Systems, 2019 Vol.11 No.3, pp.374 - 385

Received: 31 Jan 2017
Accepted: 03 May 2017
Published online: 02 May 2019 *

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