Fuzzy vault-based biometric security method for tele-health monitoring systems. (October 2018)

• Record Type: Journal Article
• Authors: Pirbhulal, Sandeep; Shang, Peng; Wu, Wanqing; Sangaiah, Arun Kumar; Samuel, Oluwarotimi Williams; Li, Guanglin
• Is Part Of: Computers & electrical engineering. Volume 71(2018)
• Page Start: 546
• Page End: 557
• Publication Date: 2018-10
• Subjects: Fuzzy vault -- Biometric -- Entity recognition -- Tele-health monitoring -- Security -- Medical applications; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.08.004
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Fuzzy vault-based security method for tele-health monitoring systems. Time-domain features based entity recognition in WBSNs. The balance between security and processing time to secure medical applications. Abstract: This study proposed an efficient fuzzy vault-based security method that adopts time-domain parameters extracted from physiological signals for tele-healthcare applications, including rehabilitation and stress management systems among others. One of the main challenges of the existing fuzzy vault-based approaches is that they depend on frequency-domain parameters of bio-signals to generate entities identifiers which require more processing time and energy consumption. Hence, we firstly designed a wearable platform for bio-signals collection, and later extracted time-domain features from the signals to generate efficient and distinctive identifiers for securing medical data in tele-healthcare applications. The statistical tests and hamming distances were applied to verify the performance of the generated identifiers regarding their uniqueness and randomness, respectively. This research work considered a total of 30 subjects data and the experimental results reveal that the proposed approach has better performance in WBSNs regarding processing time (0.168 ms) and energy consumption (1.423 mJ) than the traditional techniques.