Publication Type
Journal Article
Version
acceptedVersion
Publication Date
10-2022
Abstract
Secure outsourced computation is a key technique for protecting data security and privacy in the cloud. Although fully homomorphic encryption (FHE) enables computations over encrypted data, it suffers from high computation costs in order to support an unlimited number of arithmetic operations. Recently, secure computations based on interactions of multiple computation servers and partially homomorphic encryption (PHE) were proposed in the literature, which enable an unbound number of addition and multiplication operations on encrypted data more efficiently than FHE and do not add any noise to encrypted data; however, these existing solutions are either limited in functionalities (e.g., computation on natural numbers only) or leak information of the underlying data. To tackle these shortcomings, this paper proposes Secure Outsourced Computation on Integers (SOCI) based on PHE and a twin-server architecture. Compared with the existing solutions, SOCI supports computations on encrypted integers (vs. natural numbers) and greatly improves the security and correctness of the computations. Results of theoretical analysis and experimental evaluation show that SOCI outperforms existing solutions in computation and communication efficiencies.
Keywords
Cloud computing, cryptographic protocols, cryptography, data privacy, secure computation
Discipline
Databases and Information Systems | Information Security
Research Areas
Cybersecurity
Publication
IEEE Transactions on Information Forensics and Security
Volume
17
First Page
3637
Last Page
3648
ISSN
1556-6013
Identifier
10.1109/TIFS.2022.3211707
Publisher
Institute of Electrical and Electronics Engineers
Citation
ZHAO, Bowen; YUAN, Jiaming; LIU, Ximeng; WU, Yongdong; PANG, Hwee Hwa; and DENG, Robert H..
SOCI: A Toolkit for Secure Outsourced Computation on Integers. (2022). IEEE Transactions on Information Forensics and Security. 17, 3637-3648.
Available at: https://ink.library.smu.edu.sg/sis_research/7587
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/TIFS.2022.3211707