Literature DB >> 26540720

Enhancing Heart-Beat-Based Security for mHealth Applications.

Robert M Seepers, Christos Strydis, Ioannis Sourdis, Chris I De Zeeuw.   

Abstract

In heart-beat-based security, a security key is derived from the time difference between consecutive heart beats (the inter-pulse interval, IPI), which may, subsequently, be used to enable secure communication. While heart-beat-based security holds promise in mobile health (mHealth) applications, there currently exists no work that provides a detailed characterization of the delivered security in a real system. In this paper, we evaluate the strength of IPI-based security keys in the context of entity authentication. We investigate several aspects that should be considered in practice, including subjects with reduced heart-rate variability (HRV), different sensor-sampling frequencies, intersensor variability (i.e., how accurate each entity may measure heart beats) as well as average and worst-case-authentication time. Contrary to the current state of the art, our evaluation demonstrates that authentication using multiple, less-entropic keys may actually increase the key strength by reducing the effects of intersensor variability. Moreover, we find that the maximal key strength of a 60-bit key varies between 29.2 bits and only 5.7 bits, depending on the subject's HRV. To improve security, we introduce the inter-multi-pulse interval (ImPI), a novel method of extracting entropy from the heart by considering the time difference between nonconsecutive heart beats. Given the same authentication time, using the ImPI for key generation increases key strength by up to 3.4 × (+19.2 bits) for subjects with limited HRV, at the cost of an extended key-generation time of 4.8 × (+45 s).

Mesh:

Year:  2015        PMID: 26540720     DOI: 10.1109/JBHI.2015.2496151

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  3 in total

Review 1.  Technological Requirements and Challenges in Wireless Body Area Networks for Health Monitoring: A Comprehensive Survey.

Authors:  Lisha Zhong; Shuling He; Jinzhao Lin; Jia Wu; Xi Li; Yu Pang; Zhangyong Li
Journal:  Sensors (Basel)       Date:  2022-05-06       Impact factor: 3.847

2.  Heartbeats Do Not Make Good Pseudo-Random Number Generators: An Analysis of the Randomness of Inter-Pulse Intervals.

Authors:  Lara Ortiz-Martin; Pablo Picazo-Sanchez; Pedro Peris-Lopez; Juan Tapiador
Journal:  Entropy (Basel)       Date:  2018-01-30       Impact factor: 2.524

3.  Real-Time Learning from an Expert in Deep Recommendation Systems with Application to mHealth for Physical Exercises.

Authors:  Arash Mahyari; Peter Pirolli; Jacqueline A LeBlanc
Journal:  IEEE J Biomed Health Inform       Date:  2022-08-11       Impact factor: 7.021

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.