Publication Type
Conference Proceeding Article
Version
submittedVersion
Publication Date
3-2004
Abstract
To support ubiquitous computing, the underlying data have to be persistent and available anywhere-anytime. The data thus have to migrate from devices local to individual computers, to shared storage volumes that are accessible over open network. This potentially exposes the data to heightened security risks. We propose two mechanisms, in the context of a steganographic file system, to mitigate the risk of attacks initiated through analyzing data accesses from user applications. The first mechanism is intended to counter attempts to locate data through updates in between snapshots - in short, update analysis. The second mechanism prevents traffic analysis - identifying data from I/O traffic patterns. We have implemented the first mechanism on Linux and conducted experiments to demonstrate its effectiveness and practicality. Simulation results on the second mechanism also show its potential for real world applications.
Keywords
I/O traffic patterns, Linux, steganographic file system, ubiquitous computing
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Publication
ICDE 2004: 20th IEEE International Conference on Data Engineering: Proceedings: 30 March-2 April, 2004, Boston
First Page
572
Last Page
583
ISBN
9780769520650
Identifier
10.1109/ICDE.2004.1320028
Publisher
IEEE
City or Country
Boston, USA
Citation
ZHOU, Xuan; PANG, Hwee Hwa; and TAN, Kian-Lee.
Hiding Data Accesses in Steganographic File System. (2004). ICDE 2004: 20th IEEE International Conference on Data Engineering: Proceedings: 30 March-2 April, 2004, Boston. 572-583.
Available at: https://ink.library.smu.edu.sg/sis_research/1142
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.ieeecomputersociety.org/10.1109/ICDE.2004.1320028
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons