Profile Matching for IoMT: A Verifiable Private Set Intersection Scheme

Yalian Qian, Jian Shen*, Pandi Vijayakumar, Pradip Kumar Sharma

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


The rapid development of the Internet of Things (IoTs), 5 G and artificial intelligence (AI) technology have been dramatically incentivizing the advancement of Internet of Medical Things (IoMT) in recent years. Profile matching technology can be used to realize the sharing of medical information between patients by matching similar symptom attributes. However, the symptom attributes are associated with patients' sensitive information such as gender, age, physiological data, and other personal health information, thus the privacy of patients will be revealed during the matching process in the IoMT. To solve the problem, this paper proposes a verifiable private set intersection scheme to achieve fine-grained profile matching. On the one hand, the privacy data of patients can be divided by multi-tag to implement fine-grained operations. On the other hand, re-encryption technique is utilized to protect the privacy of patients. In addition, the cloud server may violate the scheme, thus a verifiable mechanism is leveraged to check the correctness of computation. The analysis of security indicates that our proposed scheme can resist the untrusted cloud server and the performance simulation demonstrates that our scheme improves efficiency by reducing the use of bilinear pairs.

Original languageEnglish
Pages (from-to)3794-3803
Number of pages10
JournalIEEE journal of biomedical and health informatics
Issue number10
Early online date10 Jun 2021
Publication statusPublished - 1 Oct 2021

Bibliographical note

Funding Information:
This work was supported by the National Natural Science Foundation of China under Grants U1836115, 61672295, 61922045, 61672290, 61877034, the Natural Science Foundation of Jiangsu Province under Grant No. BK20181408, the Peng Cheng Laboratory Project of Guangdong Province PCL2018KP004, the CICAEET fund, and the PAPD fund.


  • Internet of Medical Things
  • privacy protection
  • private set intersection
  • profile matching


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