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Provably Secure Three-Factor-Based Mutual Authentication Scheme with PUF for Wireless Medical Sensor Networks.

Deok Kyu KwonYoHan ParkYoung Ho Park
Published in: Sensors (Basel, Switzerland) (2021)
Wireless medical sensor networks (WMSNs) are used in remote medical service environments to provide patients with convenient healthcare services. In a WMSN environment, patients wear a device that collects their health information and transmits the information via a gateway. Then, doctors make a diagnosis regarding the patient, utilizing the health information. However, this information can be vulnerable to various security attacks because the information is exchanged via an insecure channel. Therefore, a secure authentication scheme is necessary for WMSNs. In 2021, Masud et al. proposed a lightweight and anonymity-preserving user authentication scheme for healthcare environments. We discover that Masud et al.'s scheme is insecure against offline password guessing, user impersonation, and privileged insider attacks. Furthermore, we find that Masud et al.'s scheme cannot ensure user anonymity. To address the security vulnerabilities of Masud et al.'s scheme, we propose a three-factor-based mutual authentication scheme with a physical unclonable function (PUF). The proposed scheme is secure against various security attacks and provides anonymity, perfect forward secrecy, and mutual authentication utilizing biometrics and PUF. To prove the security features of our scheme, we analyze the scheme using informal analysis, Burrows-Abadi-Needham (BAN) logic, the Real-or-Random (RoR) model, and Automated Verification of Internet Security Protocols and Applications (AVISPA) simulation. Furthermore, we estimate our scheme's security features, computation costs, communication costs, and energy consumption compared with the other related schemes. Consequently, we demonstrate that our scheme is suitable for WMSNs.
Keyphrases
  • health information
  • healthcare
  • visible light
  • social media
  • global health
  • mental health
  • primary care
  • machine learning
  • newly diagnosed
  • high throughput
  • public health
  • ejection fraction
  • case report