Publication Type
Journal Article
Version
acceptedVersion
Publication Date
12-2016
Abstract
In this paper, we propose a framework for privacy-preserving outsourced functional computation across large-scale multiple encrypted domains, which we refer to as POFD. With POFD, a user can obtain the output of a function computed over encrypted data from multiple domains while protecting the privacy of the function itself, its input and its output. Specifically, we introduce two notions of POFD, the basic POFD and its enhanced version, in order to tradeoff the levels of privacy protection and performance. We present three protocols, named Multi-domain Secure Multiplication protocol (MSM), Secure Exponent Calculation protocol with private Base (SECB), and Secure Exponent Calculation protocol ( SEC), as the core sub-protocols for POFD to securely compute the outsourced function. Detailed security analysis shows that the proposed POFD achieves the goal of calculating a user-defined function across different encrypted domains without privacy leakage to unauthorized parties. Our performance evaluations using simulations demonstrate the utility and the efficiency of POFD.
Keywords
multiple encrypted domains, Privacy-preserving, function privacy, homomorphic encryption, outsourced computation, large-scale
Discipline
Information Security
Research Areas
Cybersecurity
Publication
IEEE Transactions on Computers
Volume
65
Issue
12
First Page
3567
Last Page
3579
ISSN
0018-9340
Identifier
10.1109/TC.2016.2543220
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
LIU, Ximeng; QIN, Baodong; DENG, Robert H.; LU, Rongxing; and MA, Jianfeng.
A privacy-preserving outsourced functional computation framework across large-scale multiple encrypted domains. (2016). IEEE Transactions on Computers. 65, (12), 3567-3579.
Available at: https://ink.library.smu.edu.sg/sis_research/3346
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TC.2016.2543220