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Computation Outsourcing Meets Lossy Channel: Secure Sparse Robustness Decoding Service in Multi-Clouds
Yushu Zhang1,2; Jiantao Zhou3; Yong Xiang2; Leo Yu Zhang4; Fei Chen5; Shaoning Pang6; Xiaofeng Liao1
2021-09
Source PublicationIEEE Transactions on Big Data
ISSN2332-7790
Volume7Issue:4Pages:717-728
Abstract

This paper addresses the problem of lossy outsourcing, i.e., clients outsource computation needs to the cloud side through lossy channels, which is very common in practice. We focus on the case that the clients transmit 2D sparse signals to the semi-trusted clouds over packet-loss networks, and the clouds provide sparse robustness decoding service (SRDS) for the users. In order to achieve high level of efficiency and security, we propose to jointly exploit parallel compressive sensing for robust signal encoding and employ multiple cloud servers for SRDS. Specifically, prior to encoding, a signal is encrypted by only altering the indices and amplitudes of its non-zero entries. The encrypted signal is sensed using a Gaussian measurement matrix and the generated compressive measurements are then sent to multi-clouds for SRDS, along with the occurrence of packet loss. Each column in compressive measurements can be regarded as a packet and each description consists of a certain number of packets. Each description together with a small portion of support set is distributed to a cloud. When receiving the request from a user, each cloud performs SRDS using the acquired description, where the reconstructed signal is still in encrypted form so that the signal privacy is well preserved. After receiving the reconstructed signal, the user accomplishes the decryption operation. Experimental results show that the encryption algorithm improves compressibility and reconstruction performance compared with the case of no encryption, and the proposed privacy-assured outsourcing of SRDS is highly robust and efficient

KeywordParallel Compressive Sensing Sparse Robustness Decoding Service Packet-loss Cloud Computing
DOI10.1109/TBDATA.2017.2711040
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems ; Computer Science, Theory & Methods
WOS IDWOS:000683988500010
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Citation statistics
Cited Times [WOS]:5   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Faculty of Science and Technology
Corresponding AuthorYushu Zhang
Affiliation1.the Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, School of Electronics and Information Engineering, Southwest University, Chongqing 400715, China
2.the School of Information Technology, Deakin University, Victoria 3125, Australia
3.the Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau
4.the Department of Electronic Engineering, City University of Hong Kong, Hong Kong
5.the College of Computer Science and Engineering, Shenzhen University, Shenzhen 518060, China
6.the Department of Computing, Unitec Institute of Technology, Private Bag 92025, Auckland, New Zealand
Recommended Citation
GB/T 7714
Yushu Zhang,Jiantao Zhou,Yong Xiang,et al. Computation Outsourcing Meets Lossy Channel: Secure Sparse Robustness Decoding Service in Multi-Clouds[J]. IEEE Transactions on Big Data,2021,7(4):717-728.
APA Yushu Zhang,Jiantao Zhou,Yong Xiang,Leo Yu Zhang,Fei Chen,Shaoning Pang,&Xiaofeng Liao.(2021).Computation Outsourcing Meets Lossy Channel: Secure Sparse Robustness Decoding Service in Multi-Clouds.IEEE Transactions on Big Data,7(4),717-728.
MLA Yushu Zhang,et al."Computation Outsourcing Meets Lossy Channel: Secure Sparse Robustness Decoding Service in Multi-Clouds".IEEE Transactions on Big Data 7.4(2021):717-728.
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