Confidentiality-Preserving Publicly Verifiable Computation Schemes for Polynomial Evaluation and Matrix-Vector Multiplication
Joint Authors
Ma, Jixin
Zhu, Binrui
Sun, Jiameng
Qin, Jing
Hu, Jiankun
Source
Security and Communication Networks
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-06-21
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Information Technology and Computer Science
Abstract EN
With the development of cloud services, outsourcing computation tasks to a commercial cloud server has drawn attention of various communities, especially in the Big Data era.
Public verifiability offers a flexible functionality in real circumstance where the cloud service provider (CSP) may be untrusted or some malicious users may slander the CSP on purpose.
However, sometimes the computational result is sensitive and is supposed to remain undisclosed in the public verification phase, while existing works on publicly verifiable computation (PVC) fail to achieve this requirement.
In this paper, we highlight the property of result confidentiality in publicly verifiable computation and present confidentiality-preserving public verifiable computation (CP-PVC) schemes for multivariate polynomial evaluation and matrix-vector multiplication, respectively.
The proposed schemes work efficiently under the amortized model and, compared with previous PVC schemes for these computations, achieve confidentiality of computational results, while maintaining the property of public verifiability.
The proposed schemes proved to be secure, efficient, and result-confidential.
In addition, we provide the algorithms and experimental simulation to show the performance of the proposed schemes, which indicates that our proposal is also acceptable in practice.
American Psychological Association (APA)
Sun, Jiameng& Zhu, Binrui& Qin, Jing& Hu, Jiankun& Ma, Jixin. 2018. Confidentiality-Preserving Publicly Verifiable Computation Schemes for Polynomial Evaluation and Matrix-Vector Multiplication. Security and Communication Networks،Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1214207
Modern Language Association (MLA)
Sun, Jiameng…[et al.]. Confidentiality-Preserving Publicly Verifiable Computation Schemes for Polynomial Evaluation and Matrix-Vector Multiplication. Security and Communication Networks No. 2018 (2018), pp.1-15.
https://search.emarefa.net/detail/BIM-1214207
American Medical Association (AMA)
Sun, Jiameng& Zhu, Binrui& Qin, Jing& Hu, Jiankun& Ma, Jixin. Confidentiality-Preserving Publicly Verifiable Computation Schemes for Polynomial Evaluation and Matrix-Vector Multiplication. Security and Communication Networks. 2018. Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1214207
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214207