A large-scale concurrent data anonymous batch verification scheme for mobile healthcare crowd sensing

Jingwei Liu, Huijuan Cao, Qingqing Li, Fanghui Cai, Xiaojiang Du, Mohsen Guizani

Research output: Contribution to journalArticlepeer-review

59 Scopus citations

Abstract

Recently, with the rapid development of big data, Internet of Things (IoT) brings more and more intelligent and convenient services to people's daily lives. Mobile healthcare crowd sensing (MHCS), as a typical application of IoT, is becoming an effective approach to provide various medical and healthcare services to individual or organizations. However, MHCS still have to face to different security challenges in practice. For example, how to quickly and effectively authenticate masses of bio-information uploaded by IoT terminals without revealing the owners' sensitive information. Therefore, we propose a large-scale concurrent data anonymous batch verification scheme for MHCS based on an improved certificateless aggregate signature. The proposed scheme can authenticate all sensing bio-information at once in a privacy preserving way. The individual data generated by different users can be verified in batch, while the actual identity of participants is hidden. Moreover, assuming the intractability of computational Diffie-Hellman problem, our scheme is proved to be secure. Finally, the performance evaluation shows that the proposed scheme is suitable for MHCS, due to its high efficiency.

Original languageEnglish
Article number8341503
Pages (from-to)1321-1330
Number of pages10
JournalIEEE Internet of Things Journal
Volume6
Issue number2
DOIs
StatePublished - Apr 2019

Keywords

  • Aggregate signature (AS)
  • batch verification
  • mobile healthcare crowd sensing (MHCS)
  • privacy preservation

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