Applying Motion Sensor Data from Diverse Mobile Devices to Generate Pure Random Numbers

Deep Kumar Bangotra¹

Section:Research Paper, Product Type: Journal-Paper
Vol.8 , Issue.9 , pp.90-92, Sep-2022

Online published on Sep 30, 2022

Copyright © Deep Kumar Bangotra . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

Abstract :
The random numbers have inherent susceptibility to be reverse engineered. Therefore, the production of pseudorandom numbers is not a completely reliable process. In anyway, random number generators should not employ a seed value and should produce unpredictably generated numbers. This paper presents a way for building a pure random number generator that is highly effective, modern, and impossible to reverse-engineer. Additionally, it does not require the installation of any special equipment. It can be used to meet the needs of any user and has a lower processing complexity. In this paper, we make use of information gathered from several mobile motion sensor devices. After combining the data, an algorithm is run through it to produce a data entropy pool with a list of random values. Then, in order to generate random numbers in the appropriate range, a pseudo random number generation process is utilised with this pure random data set as a seed. Since modern smartphones are well-equipped with sensors, this solution did not require any specialised hardware.

Key-Words / Index Term :
Pure random number generation, Reduced Complexity, Algorithm.

References :
[1] Intel ® Digital Random Generator Generat or (DRNG), Revision 1. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.intel.com/content/dam/develop/external/us/en/documents/441-intel-r-drng-software-implementation-guide-final-aug7.pdf, 2012.
[2] “Quantum Random Number Generation (QRNG) - ID Quantique.” [Online]. Available: https://www.idquantique.com/random-number-generation/overview/. [Accessed: 08-Oct-2022].
[3] C. Tokunaga, D. Blaauw, and T. Mudge, “True Random Number Generator With a Metastability-Based Quality Control,” IEEE Journal of Solid-State Circuits, vol. 43, no. 1, pp. 78–85, 2008.
[4] Z. Huang and H. Chen, “A truly random number generator based on thermal noise,” International Conference on ASIC, Proceedings, pp. 862–864, 2001.
[5] S. Buchovecka, R. Lorencz, F. Kodytek, and J. Bucek, “True Random Number Generator Based on ROPUF Circuit,” Proceedings - 19th Euromicro Conference on Digital System Design, DSD 2016, pp. 519–523, 2016.