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An efficient privacy-preserving compressive data gathering scheme in WSNs

  • Kun Xie
  • , Xueping Ning
  • , Xin Wang
  • , Shiming He
  • , Zuoting Ning
  • , Xiaoxiao Liu
  • , Jigang Wen
  • , Zheng Qin
  • Hunan University
  • Stony Brook University
  • Changsha University of Science and Technology
  • State Grid Corporation of China
  • CAS - Institute of Computing Technology

Research output: Contribution to journalArticlepeer-review

77 Scopus citations

Abstract

Because of the strict energy limitation and the common vulnerability of Wireless Sensor Networks (WSNs), providing efficient and secure data gathering in WSNs becomes an essential problem. Compressive data gathering, which is based on the recent breakthroughs in compressive sensing theory, has been proposed as a viable approach for data gathering in WSNs at low communication overhead. Nevertheless, compressive data gathering is susceptible to various attacks in the presence of the open wireless medium. In this paper, we propose a novel Efficient Privacy-Preserving Compressive Data Gathering Scheme, which exploits homomorphic encryption functions in compressive data gathering to thwart the traffic analysis/flow tracing and realize the privacy preservation. This allows the proposed scheme to possess the two important privacy-preserving features of message flow untraceability and message content confidentiality. Extensive performance evaluations and security analyses demonstrate the validity and efficiency of the proposed scheme.

Original languageEnglish
Pages (from-to)82-94
Number of pages13
JournalInformation Sciences
Volume390
DOIs
StatePublished - Jun 1 2017

Keywords

  • Compressive sensing
  • Homomorphic encryption function
  • Privacy-preserving
  • WSNs

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