A privacy-preserving cloud-based data management system with efficient revocation scheme
by Shih-Chien Chang; Ja-Ling Wu
International Journal of Computational Science and Engineering (IJCSE), Vol. 20, No. 2, 2019

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.

Online publication date: Fri, 29-Nov-2019

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