Title: A privacy-preserving cloud-based data management system with efficient revocation scheme

Authors: Shih-Chien Chang; Ja-Ling Wu

Addresses: Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan ' Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan

Abstract: There are lots of data management systems, according to various reasons, designating their high computational work-loads to public cloud service providers. It is well-known that once we entrust our tasks to a cloud server, we may face several threats, such as privacy-infringement with regard to users' attribute information; therefore, an appropriate privacy preserving mechanism is a must for constructing a secure cloud-based data management system (SCBDMS). To design a reliable SCBDMS with server-enforced revocation ability is a very challenging task even if the server is working under the honest-but-curious mode. In existing data management systems, privacy-preserving revocation service is seldom provided, especially when it is outsourced to a third party. In this work, with the aids of oblivious transfer and the newly proposed stateless lazy re-encryption (SLREN) mechanism, a SCBDMS, with secure, reliable and efficient server-enforced attribute revocation ability is built. Comparing with related works, our experimental results show that, in the newly constructed SCBDMS the storage-requirement of the cloud server and the communication overheads between cloud server and systems users are largely reduced, due to the nature of late involvement of SLREN.

Keywords: privacy-preserving; lazy re-encryption; revocation.

DOI: 10.1504/IJCSE.2019.103819

International Journal of Computational Science and Engineering, 2019 Vol.20 No.2, pp.190 - 199

Received: 03 Apr 2018
Accepted: 26 Oct 2018
Published online: 29 Nov 2019 *

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