İnsan Hareketleri Tabanlı Gerçek Rasgele Sayı Üretimi

Gerçek rasgele sayı üreteci (GRSÜ) ile rasgele sayı üretmek için deterministik olmayan bir gürültü kaynağındanyararlanılır. Rasgelelik derecesinin daha yüksek olması nedeniyle GRSÜ, Sözde rasgele sayı üreticisinden dahagüvenli 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 tabanlıbir mobil cihazdan örneklenerek elde edilmiştir. Daha sonra, elde edilen bu işaretler, normalizasyon işlemiuygulanarak ham sayı dizilerine dönüştürülmüştür. Son olarak, sayı dizilerinin istatistiksel özellikleriniiyileştirmek için XOR son işlemi uygulanmış ve rasgele sayı üretimi gerçekleştirilmiştir. Kişi yürürken, koşarkenve stabil konumda iken sensörlerden elde edilen toplamda 15 veri seti oluşturulmuştur. Sayıların istatistikselözellikleri NIST test süiti, Skala İndeks ve otokorelasyon ile incelenmiştir. Önerilen GRSÜ, cep telefonu platformuiçin uygun, evrensel ve düşük maliyetli olup kişiye özgü rasgele sayı üretmektedir.

True Random Number Generation Based on Human Movements

A non-deterministic noise source is used to generate the random number with the real random number generator (TRNG). Because the degree of randomness is higher, TRNG produces a more secure number than the PRNG number generator. In this article, we propose a TRNG that produces random numbers with human movements. The proposed TRNG uses the acceleration and GPS sensors in almost all people's mobile phones. First, the acceleration and position changes resulting from the movements of the person carrying the mobile phone in the 3- D environment are obtained by sampling from an android based mobile device. Then, these obtained marks are converted into raw number sequences by normalization process. Finally, to improve the statistical properties of the number sequences, XOR finishing was performed and random number generation was performed. A total of 15 data sets were generated from the sensors while walking, running and in stable position. The statistical properties of the numbers were examined by NIST test suite, Scale Index and autocorrelation. The proposed TRNG is suitable for mobile phone platform, universal and low cost, and it is possible to produce random number of the unique number.

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