Publication Type
Journal Article
Version
publishedVersion
Publication Date
11-2021
Abstract
With the ubiquity of mobile devices and rapid development of cloud computing, mobile cloud computing (MCC) has been considered as an essential computation setting to support complicated, scalable and flexible mobile applications by overcoming the physical limitations of mobile devices with the aid of cloud. In the MCC setting, since many mobile applications (e.g., map apps) interacting with cloud server and application server need to perform computation with the private data of users, it is important to realize secure computation for MCC. In this article, we propose an efficient server-aided secure two-party computation (2PC) protocol for MCC. This is the first work that considers collusion between a malicious garbled circuit evaluator and a semi-honest server while ensuring privacy and correctness. Also, it can guarantee fairness when collusion does not exist. The security analysis shows that our protocol can securely compute any function f(x, y) against different types of adversaries in the malicious model. Also, the experimental performance analysis shows that this work outperforms the previous works for at least 10 times with the same security level.
Keywords
Secure two-party computation, server-aided computation, mobile cloud computing, garbled circuit
Discipline
Data Storage Systems | Information Security
Research Areas
Information Systems and Management
Publication
IEEE Transactions on Dependable and Secure Computing
Volume
18
Issue
6
First Page
2820
Last Page
2834
ISSN
1545-5971
Identifier
10.1109/TDSC.2020.2966632
Publisher
Institute of Electrical and Electronics Engineers
Citation
WU, Yulin; WANG, Xuan; SUSILO, Willy; YANG, Guomin; JIANG, Zoe L.; CHEN, Qian; and XU, Peng.
Efficient server-aided secure two-party computation in heterogeneous mobile cloud computing. (2021). IEEE Transactions on Dependable and Secure Computing. 18, (6), 2820-2834.
Available at: https://ink.library.smu.edu.sg/sis_research/7295
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1109/TDSC.2020.2966632