Evaluation of ECG random number generator for wireless body sensor networks security

Chen, Xin; Zhang, Yongxiang; Zhang, Guanghe; Zhang, Yuan-Ting

doi:10.1109/bmei.2012.6513218

Cited by 11 publications

(7 citation statements)

References 7 publications

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“…Her iki yaklaşım ile elde edilen sayılar NIST test suiti ile analiz edilmiş başarılı sonuçlar elde edilmiştir. Chen ve arkadaşları kriptografik sistemler için ECG sinyallerini kullanarak SRSÜ tabanlı sayı üreteci geliştirdiler [8]. Geliştirilen sayı üreteci literatürde bilinen dokuz SRSÜ yapısı ile karşılaştırılmıştır.…”

Section: Introductionunclassified

İnsan Hareketleri Tabanlı Gerçek Rasgele Sayı Üretimi

Tuncer

Genc²

2019

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Gerçek rasgele sayı üreteci (GRSÜ) ile rasgele sayı üretmek için deterministik olmayan bir gürültü kaynağından yararlanılır. Rasgelelik derecesinin daha yüksek olması nedeniyle GRSÜ, Sözde rasgele sayı üreticisinden daha güvenli sayı üretir. Bu makalede, insan hareketleri ile rasgele sayı üreten bir GRSÜ öneriyoruz. Önerilen GRSÜ, hemen hemen tüm insanların kullandığı mobil telefonlardaki ivme ve GPS sensörlerini kullanmaktadır. İlk olarak, mobil telefonu taşıyan kişinin 3-D ortamdaki hareketleri sonucunda ivme ve konum değişimleri android tabanlı bir mobil cihazdan örneklenerek elde edilmiştir. Daha sonra, elde edilen bu işaretler, normalizasyon işlemi uygulanarak ham sayı dizilerine dönüştürülmüştür. Son olarak, sayı dizilerinin istatistiksel özelliklerini iyileştirmek için XOR son işlemi uygulanmış ve rasgele sayı üretimi gerçekleştirilmiştir. Kişi yürürken, koşarken ve stabil konumda iken sensörlerden elde edilen toplamda 15 veri seti oluşturulmuştur. Sayıların istatistiksel özellikleri NIST test süiti, Skala İndeks ve otokorelasyon ile incelenmiştir. Önerilen GRSÜ, cep telefonu platformu için uygun, evrensel ve düşük maliyetli olup kişiye özgü rasgele sayı üretmektedir.

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Section: Introductionunclassified

İnsan Hareketleri Tabanlı Gerçek Rasgele Sayı Üretimi

Tuncer

Genc²

2019

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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“…Considering that a regular heart beats at 50–100 bits per minute (bpm) , the key generation process would take between 20 and 40 s. To prove that the extracted bits have a certain level of randomness, most works use either the common Shannon or Rényi entropies [ 29 ], which are not enough to claim the randomness property of a sequence of beats. Additionally, in [ 6 , 26 , 27 , 47 , 51 , 52 ], the authors remark about the same claims about the randomness of the IPIs by running the NIST STS battery of randomness tests, whereas in [ 8 ], the authors rely on the ENT suite. Table 1 summarizes the datasets that the existing works in this area have used.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, in the last column, the number of executed tests can be seen where, for instance, NIST STS (5/15) means that authors have run five tests out of the 15 that the NIST STS suite has. Note that [ 52 ] is the only work where the authors ran all tests of which NIST STS is composed. We were not able to find the main reasons for running a subset of tests in the rest of the works that use NIST STS.…”

Section: Introductionmentioning

confidence: 99%

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

Ortiz-Martin

Picazo-Sanchez

Peris-López

et al. 2018

Entropy

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Abstract:The proliferation of wearable and implantable medical devices has given rise to an interest in developing security schemes suitable for these systems and the environment in which they operate. One area that has received much attention lately is the use of (human) biological signals as the basis for biometric authentication, identification and the generation of cryptographic keys. The heart signal (e.g., as recorded in an electrocardiogram) has been used by several researchers in the last few years. Specifically, the so-called Inter-Pulse Intervals (IPIs), which is the time between two consecutive heartbeats, have been repeatedly pointed out as a potentially good source of entropy and are at the core of various recent authentication protocols. In this work, we report the results of a large-scale statistical study to determine whether such an assumption is (or not) upheld. For this, we have analyzed 19 public datasets of heart signals from the Physionet repository, spanning electrocardiograms from 1353 subjects sampled at different frequencies and with lengths that vary between a few minutes and several hours. We believe this is the largest dataset on this topic analyzed in the literature. We have then applied a standard battery of randomness tests to the extracted IPIs. Under the algorithms described in this paper and after analyzing these 19 public ECG datasets, our results raise doubts about the use of IPI values as a good source of randomness for cryptographic purposes. This has repercussions both in the security of some of the protocols proposed up to now and also in the design of future IPI-based schemes.

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“…Recently, there have been studies performed on random number generation from human-based noise sources [ 8 – 12 ]. Elham et al showed that two different people would produce different random numbers and that these numbers could be used as biometric signatures [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…The security analyses of keys obtained by both approaches were tested with distinctiveness, randomness, temporal variance, and NIST and successful results were obtained [ 11 ]. In the study performed by Chen et al [ 12 ], random number generation was done from ECG signals and the analysis was tested by NIST test suite. It was revealed by the authors that the PRNG-based generated numbers had more successful results in classical PRNG structures.…”

Section: Introductionmentioning

confidence: 99%

True Random Number Generation from Bioelectrical and Physical Signals

Tuncer

2018

Computational and Mathematical Methods in Medicine

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It is possible to generate personally identifiable random numbers to be used in some particular applications, such as authentication and key generation. This study presents the true random number generation from bioelectrical signals like EEG, EMG, and EOG and physical signals, such as blood volume pulse, GSR (Galvanic Skin Response), and respiration. The signals used in the random number generation were taken from BNCIHORIZON2020 databases. Random number generation was performed from fifteen different signals (four from EEG, EMG, and EOG and one from respiration, GSR, and blood volume pulse datasets). For this purpose, each signal was first normalized and then sampled. The sampling was achieved by using a nonperiodic and chaotic logistic map. Then, XOR postprocessing was applied to improve the statistical properties of the sampled numbers. NIST SP 800-22 was used to observe the statistical properties of the numbers obtained, the scale index was used to determine the degree of nonperiodicity, and the autocorrelation tests were used to monitor the 0-1 variation of numbers. The numbers produced from bioelectrical and physical signals were successful in all tests. As a result, it has been shown that it is possible to generate personally identifiable real random numbers from both bioelectrical and physical signals.

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Evaluation of ECG random number generator for wireless body sensor networks security

Cited by 11 publications

References 7 publications

İnsan Hareketleri Tabanlı Gerçek Rasgele Sayı Üretimi

İnsan Hareketleri Tabanlı Gerçek Rasgele Sayı Üretimi

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

True Random Number Generation from Bioelectrical and Physical Signals

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