Distributed compressed sensing for multi-sourced fusion and secure signal processing in private cloud

In this paper, a novel scheme is proposed for multi-sourced signal fusion and secure processing. Within a distributed compressed sensing (DCS) framework, traditional sampling, compression and encryption for signal acquisition are unified under the secure multiparty computation protocol. In the propo...

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Bibliographic Details
Published inMultidimensional systems and signal processing Vol. 27; no. 4; pp. 891 - 908
Main Authors Zhao, Huimin, Wei, Wenguo, Cai, Jun, Lei, Fangyuan, Luo, Jianzhen
Format Journal Article
LanguageEnglish
Published New York Springer US 01.10.2016
Subjects
Artificial Intelligence
Circuits and Systems
Electrical Engineering
Engineering
Signal,Image and Speech Processing
Multi-sourced fusion
Cloud computing
Distributed compressed sensing
Measurement matrix
Secure signal processing
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Summary:In this paper, a novel scheme is proposed for multi-sourced signal fusion and secure processing. Within a distributed compressed sensing (DCS) framework, traditional sampling, compression and encryption for signal acquisition are unified under the secure multiparty computation protocol. In the proposed scheme, generation of the pseudo-random sensing matrix offers a natural method for data encryption in DCS, allowing for joint recovery of multiparty data at legal users’ side. Experimental analysis and results indicate that the secure signal processing and recovery in DCS domain is feasible, and requires fewer measurements than the achievable approach of separate CS and Nyquist processing. The proposed scheme can be also extended to other cloud-based collaborative secure signal processing and data-mining applications.
ISSN:0923-6082
1573-0824
DOI:10.1007/s11045-015-0371-2