Paper Info
Title
A Privacy-Preserving Cloud-Based Data Management System With Efficient Revocation Scheme
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.
Year
DOI
Venue
2019
10.1504/IJCSE.2019.103819
INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING
Keywords
DocType
Volume
privacy-preserving, lazy re-encryption, revocation
Journal
20
Issue
ISSN
Citations 
2
1742-7185
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Shih-Chien Chang100.34
Ja-ling Wu21569168.11