Deakin University

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Support-set-assured parallel outsourcing of sparse reconstruction service for compressive sensing in multi-clouds

conference contribution
posted on 2016-01-04, 00:00 authored by Yushu Zhang, J Zhou, Leo ZhangLeo Zhang, F Chen, X Lei
By leveraging the concept of signal sparsity, the new signal acquisition paradigm Compressive Sensing (CS) has successfully shifted the system complexity of the encoder to the decoder. If consideration must be given to solving the heavy decoding work while guaranteeing the privacy of the signal, one of the best choices is to outsource the sparse reconstruction service to a cloud with abundant computing resources. We propose to
outsource sparse reconstruction service to multi-clouds in parallel with an assumption that multi-clouds cannot collude with each other in private. The owner protects the 2D signals’ support-set, a set consisting of the indices of the nonzero entries in that signal, using a simple exchange primitives with low complexity and less memory rather than a full random permutation matrix. When carrying out parallel compressive sensing, this exchange primitive is equivalent to random permutation matrix, thus relaxing the RIP for 2D sparse signals with high probability. Then, the compressive measurements and support-set are distributed over multi-clouds for storage and reconstruction service. Each cloud
only has a small amount of information of both the measurements
and asymmetric support-set; therefore, the privacy of the original signal can be guaranteed.



IEEE Technical Committee on Scalable Computing. Conference (2015 : Hangzhou, China)


IEEE Technical Committee on Scalable Computing Conference


1 - 6


Institute of Electrical and Electronics Engineers


Hangzhou, China

Place of publication

Piscataway, N.J.

Start date


End date






Publication classification

E Conference publication; E1.1 Full written paper - refereed

Copyright notice

2015, IEEE



Title of proceedings

SocialSec 2015 : Proceedings of the 2015 International Symposium on Security and Privacy in Social Networks and Big Data