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Cryptographic Key Generation Using ECG Signal

Sanaz Rahimi Moosavi, Ethiopia Nigussie, Seppo Virtanen, Jouni Isoaho, Cryptographic Key Generation Using ECG Signal. In: Pietro Manzoni (Ed.), Proc. of 14th IEEE Annual Consumer Communications & Networking Conference (CCNC), 1024–1031, IEEE, 2017.

Abstract:

In this paper, two different electrocardiogram
(ECG) based cryptographic key generation approaches are proposed.
The aim is to enhance the security of body area networks
through robust key generation where keys are generated on
the fly without requiring key pre-distribution solutions. The
Interpulse Interval (IPI) feature of ECG underlays both of
the proposed approaches. The first approach is realized by
using a pseudo-random number and consecutive IPI sequences.
The second approach is realized by utilizing the Advanced
Encryption Standard (AES) algorithm and IPI as the seed
generator for the AES algorithm. The efficiency of the proposed
approaches is evaluated using real ECG data of 15 patients
obtained from the MIT-BIH Arrhythmia dataset of PhysioBank.
The security analyses of the generated keys are carried out in
terms of distinctiveness, randomness, and temporal variance as
well as using the NIST benchmark. The analyses show that
our key generation approaches provide a higher security level
in comparison to existing approaches relying only on singleton
IPI sequences. The execution times required to generate the
cryptographic keys on different processors are also examined.
The results reveal that the security level improvement comes
with a reasonable increase in key generation execution time.
Comparing to existing IPI-based approaches, our approaches
require 12.3% and 41.2% more execution time, respectively.

BibTeX entry:

@INPROCEEDINGS{inpRaNiViIs17a,
  title = {Cryptographic Key Generation Using ECG Signal},
  booktitle = {Proc. of 14th IEEE Annual Consumer Communications & Networking Conference (CCNC)},
  author = {Rahimi Moosavi, Sanaz and Nigussie, Ethiopia and Virtanen, Seppo and Isoaho, Jouni},
  editor = {Manzoni, Pietro},
  publisher = {IEEE},
  pages = {1024–1031},
  year = {2017},
  ISSN = {2331-9852},
}

Belongs to TUCS Research Unit(s): Communication Systems (ComSys)

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