Cryptographically strong random number generation using integrated CMOS photodiodes for low-cost microcontroller based applications

Cryptographically strong random number generation using integrated CMOS photodiodes for low-cost microcontroller based applications

In this work, we propose a method to generate random numbers for low-cost, low-power, resource-limited low data-rate microcontrollers using integrated CMOS photodiodes. The proposed method utilizes an integrated CMOS photodiode in the photovoltaic mode as the entropy source. The method is based on serially capturing analog values derived from the integrated CMOS photodiode. The entropy of these values increased by a custom algorithm. The proposed random number generator is devised using an integrated CMOS photodiode manufactured in 180 nm standard CMOS technology. The wide applicably of the random number generator is demonstrated by realizing it on a lowcost Arduino UNO board placed in a typical room environment. The implemented random number generator passes NIST-SP800-22 and AIS31 randomness tests at high scores. The proposed method achieved 5.4 Kbps throughput and 7.2% total significance level without any postprocessing. The test results show the high cryptographical strength of the proposed method makes it a promising alternative to the currently used random number generation algorithms in low-cost, low-resources, low-data rate microcontroller-based applications.

___

  • [1] Von Neumann J. Various Techniques Used in Connection with Random Digits. National Bureau of Standards Applied Mathematics Series 1951; 12 (13): 36-38.
  • [2] Park SK, Miller KW. Random number generators: good ones are hard to find. Communications of the ACM 1988; 31 (10): 1192–1201. doi:10.1145/63039.63042
  • [3] Langdon WB. A fast high quality pseudo random number generator for graphics processing units. In: 2008 IEEE Congress on Evolutionary Computation; Hong Kong, China; 2008. pp. 459-465.
  • [4] Tseng PH, Lee MH, Lin YH, Lung HL, Wang KC et al. ReRAM-Based Pseudo-True Random Number Generator With High Throughput and Unpredictability Characteristics. IEEE Transactions on Electron Devices 2021; 68 (4): 1593-1597. doi: 10.1109/TED.2021.3057028
  • [5] Chen Z, Zhao B, Lin H, Chen L. Etoram: A More Efficient ORAM for Secure Computation. IEEE Open Journal of the Computer Society 2020; 1 (1): 285-294. doi: 10.1109/OJCS.2020.3032020
  • [6] Hu J, Zhang Z, Pan Q. A 15-Gb/s 0.0037-mm² 0.019-pJ/Bit Full-Rate Programmable Multi-Pattern PseudoRandom Binary Sequence Generator. IEEE Transactions on Circuits and Systems II: Express Briefs 2020; 67 (9): 1499-1503. doi: 10.1109/TCSII.2020.3008567
  • [7] El-Latif AAA, Abd-El-Atty B, Venegas-Andraca SE, Elwahsh H, Piran J. Providing End-to-End Security Using Quantum Walks in IoT Networks. IEEE Access 2020; 8: 92687-92696.doi: 10.1109/ACCESS.2020.2992820
  • [8] Mathew SK, Srinivasan S, Anders MA, Kaul H, Hsu SK et al. 2.4 Gbps, 7 mW All-Digital PVT-Variation Tolerant True Random Number Generator for 45 nm CMOS High-Performance Microprocessors. IEEE Journal of Solid-State Circuits 2012; 47 (11): 2807-2821. doi: 10.1109/JSSC.2012.2217631
  • [9] Kim E, Lee M, Kim J. 8.2 8Mb/s 28Mb/mJ robust true-random-number generator in 65 nm CMOS based on differential ring oscillator with feedback resistors. In: 2017 IEEE International Solid-State Circuits Conference (ISSCC), San Francisco, CA, USA; 2017. pp. 144-145.
  • [10] Yao Y, Chen X, Kang W, Zhang Y, Zhao W. Thermal Brownian Motion of Skyrmion for True Random Number Generation. IEEE Transactions on Electron Devices 2020; 67 (6): 2553-2558. doi: 10.1109/TED.2020.2989420
  • [11] Chandrasekaran ST, Karnam VEG, Sanyal A. 0.36-mW, 52-Mbps True Random Number Generator Based on a Stochastic Delta–Sigma Modulator. IEEE Solid-State Circuits Letters 2020; 3: 190-193. doi: 10.1109/LSSC.2020.3010901
  • [12] Wang X, Liang H, Wang Y, Yao L, Guo Y et al. High-Throughput Portable True Random Number Generator Based on Jitter-Latch Structure. IEEE Transactions on Circuits and Systems I: Regular Papers 2021; 68 (2): 741-750. doi: 10.1109/TCSI.2020.3037173
  • [13] Zhao Q, Zheng W, Zhao X, Cao Y, Zhang F et al. A 108 F2/Bit Fully Reconfigurable RRAM PUF Based on Truly Random Dynamic Entropy of Jitter Noise. IEEE Transactions on Circuits and Systems I: Regular Papers 2020; 67 (11): 3866-3879. doi: 10.1109/TCSI.2020.3008407
  • [14] Avaroğlu E, Tuncer T. A novel S-box-based postprocessing method for true random number generation. Turkish Journal of Electrical Engineering and Computer Science 2020; 28 (1): 288-301. doi: 10.3906/elk-1906-194
  • [15] Liu Y, Chen C, Yang DD, Li Q, Li X. Fast True Random Number Generator Based on Chaotic Oscillation in SelfFeedback Weakly Coupled Superlattices. IEEE Access 2020; 8: 182693-182703. doi: 10.1109/ACCESS.2020.3028735
  • [16] Matsumoto M, Nishimura T. Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Transaction on Modelling and Computer Simulation 1998; 8 (1): 3–30. doi: 10.1145/272991.272995
  • [17] Kuka CS, Hu Y, Xu Q, Alkahtani M. An Innovative Near-Field Communication Security Based on the Chaos Generated by Memristive Circuits Adopted as Symmetrical Key. IEEE Access 2020; 8: 167975-167984. doi: 10.1109/ACCESS.2020.3023049
  • [18] Luo Y, Wang W, Best S, Wang Y, Xu X. A High-Performance and Secure TRNG Based on Chaotic Cellular Automata Topology. IEEE Transactions on Circuits and Systems I: Regular Papers 2020; 67 (12): 4970-4983. doi: 10.1109/TCSI.2020.3019030
  • [19] Ozkaynak F. A Novel Random Number Generator Based on Fractional Order Chaotic Chua System. Elektronika Ir Elektrotechnika 2020; 26 (1): 52-57. doi: 10.5755/j01.eie.26.1.25310
  • [20] Koyuncu İ, Şeker H, Alçın M, Tuna M. A Novel Dormand-Prince Based Hybrid Chaotic True Random Number Generator on FPGA. Balkan Journal of Electrical and Computer Engineering 2021; 9 (1):40-47. doi: 10.17694/bajece.722911
  • [21] Amirany A, Jafari K, Moaiyeri MH. True Random Number Generator for Reliable Hardware Security Modules Based on a Neuromorphic Variation-Tolerant Spintronic Structure. IEEE Transactions on Nanotechnology 2020; 19: 784-791. doi: 10.1109/TNANO.2020.3034818
  • [22] Milovančev D, Vokić N, Pacher C, Khan I, Marquardt C et al. Towards Integrating True Random Number Generation in Coherent Optical Transceivers. IEEE Journal of Selected Topics in Quantum Electronics 2020; 26 (5): 1-8. doi: 10.1109/JSTQE.2020.3004206
  • [23] Mikailov M, Sudarsan SD, Luo F. Pseudo Random Number Generation for Parallelized Jobs on Clusters. In: 2012 12th IEEE/ACM International Symposium on Cluster, Ottawa, ON, Canada; 2012. pp. 680-681.
  • [24] Huang L, Zhou H, Xie C. Quantum Random Number Generation on Alibaba Cloud Servers. 2020 IEEE Photonics Conference (IPC), Vancouver, BC, Canada; 2020, pp. 1-2.
  • [25] Zungeru AM. A Secured Smart Home Switching System based on Wireless Communications and Self-Energy Harvesting. IEEE Access 2019; 7: 25063-25085. doi: 10.1109/ACCESS.2019.2900305
  • [26] Friha O, Ferrag MA, Shu L, Maglaras L, Wang X. Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies. IEEE/CAA Journal of Automatica Sinica 2021; 8 (4): 718-752. doi: 10.1109/JAS.2021.1003925
  • [27] Herrewege AV, Verbauwhede I. Software Only, Extremely Compact, Keccak-based Secure PRNG on ARM CortexM. In: 51st Annual Design Automation Conference (DAC ’14).New York, NY, USA; 2014, pp. 1–6.
  • [28] Huth C, Becker D, Merchan JG, Duplys P, Güneysu T. Securing Systems With Indispensable Entropy: LWEBased Lossless Computational Fuzzy Extractor for the Internet of Things. IEEE Access 2017; 5: 11909-11926. doi: 10.1109/ACCESS.2017.2713835
  • [29] Wei W, Xie G, Dang A, Guo H. High-Speed and Bias-Free Optical Random Number Generator. IEEE Photonics Technology Letters 2012; 24 (6): 437-439. doi: 10.1109/LPT.2011.2180521
  • [30] Bisadi Z, Fontana G, Moser E, Pucker G, Pavesi L.Robust Quantum Random Number Generation With Silicon Nanocrystals Light Source. Journal of Lightwave Technology 2017; 35 (9): 1588-1594. doi: 10.1109/JLT.2017.2656866
  • [31] Wang A, Wang L, Wang Y. Post-processing-free 400 Gb/s true random number generation using optical heterodyne chaos. In: 2016 25th Wireless and Optical Communication Conference; Chengdu, China; 2016, pp. 1-4.
  • [32] Park BK, Park H, Kim Y, Kang J, Yeom Y et al. Practical True Random Number Generator Using CMOS Image Sensor Dark Noise. IEEE Access 2019; 7: 91407-91413. doi: 10.1109/ACCESS.2019.2926825
  • [33] AlMutairi D, Bonny T.Image Encryption Based on Chua Chaotic Oscillator. In: 3rd International Conference on Signal Processing and Information Security (ICSPIS); DUBAI, United Arab Emirates; 2020, pp. 1-4.
  • [34] Bakiri M, Guyeux C, Couchot JF, Oudjida AK. Survey on hardware implementation of random number generators on FPGA: Theory and experimental analyses. Computer Science Review 2018; 27: 135-153. doi:10.1016/j.cosrev.2018.01.002
  • [35] Bandyopadhyay D, Sen J. Internet of Things: Applications and challenges in technology and standardization, Wireless Personal Communications 2011; 58 (1): 49–69. doi: 10.1007/s11277-011-0288-5
  • [36] Daugman J. How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology 2004; 14 (1): 21-30. doi: 10.1109/TCSVT.2003.818350.
  • [37] Bennett CH, Brassard G. Quantum cryptography: Public key distribution and coin tossing. Processing IEEE International Conferance Computer Systems Signal Processing, Dec. 1984, pp. 175–179.
  • [38] Brouk I, Nemirovsky A, Nemirovsky Y. Analysis of noise in CMOS image sensor. In: 2008 IEEE International Conference on Microwaves, Tel-Aviv, Israel; 2008. pp. 1-8.
  • [39] Murari K, Etienne-Cummings R, Thakor N, Cauwenberghs G. Which Photodiode to Use: A Comparison of CMOSCompatible Structures. IEEE Sensors Journal 2009;9 (7):752-760. doi:10.1109/JSEN.2009.2021805
  • [40] Chao CY, Chen Y, Chou K, Sze J, Hsueh F et al. Extraction and Estimation of Pinned Photodiode Capacitance in CMOS Image Sensors. IEEE Journal of the Electron Devices Society 2014; 2 (4): 59-64. doi: 10.1109/JEDS.2014.2318060
  • [41] Vigna S. Further scramblings of Marsaglia’s xorshift generators. Journal of Computational and Applied Mathematics 2017; 315: 175-181. doi: 10.1016/j.cam.2016.11.006
  • [42] Migabo EM, Djouani KD, Kurien AM. The Narrowband Internet of Things (NB-IoT) Resources Management Performance State of Art, Challenges, and Opportunities.IEEE Access 2020; 8: 97658-97675. doi: 10.1109/ACCESS.2020.2995938
  • [43] Gany F, Bari S, Prasad L. Perception and reality of particulate matter exposure in New York City taxi drivers. Journal of Exposure Science Environmental Epidemiology 2017; 27: 221–226. doi: 10.1038/jes.2016.23
  • [44] Kök İ, Şimşek MU, Özdemir S. A deep learning model for air quality prediction in smart cities. In: IEEE International Conference on Big Data (Big Data), Boston, USA; 2007. pp. 1983-1990.
  • [45] Pincus SM. Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences of the United States of America 1991; 88: 2297–301. doi:10.1073/pnas.88.6.2297
  • [46] Bassham L, Rukhin A, Soto J, Nechvatal J, Smid M et al. A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications. USA: NIST, 2010.
  • [47] Pareschi F, Rovatti R, Setti G. On Statistical Tests for Randomness Included in the NIST SP800-22 Test Suite and Based on the Binomial Distribution. IEEE Transactions on Information Forensics and Security 2012; 7(2): 491-505. doi: 10.1109/TIFS.2012.2185227
  • [48] Goll M, Gueron S. Randomness Tests in Hostile Environments. IEEE Transactions on Dependable and Secure Computing 2018; 15(2): pp. 289-294. doi: 10.1109/TDSC.2016.2537799
  • [49] Killman W, Schindler W. A proposal for: Functionality classes for random number generators. Bundesamt für Sicherheit in der Informationstechnik (BSI), 2011
  • [50] Soto, Juan. (1999). Randomness Testing of the Advanced Encryption Standard Candidate Algorithms.
Turkish Journal of Electrical Engineering and Computer Sciences-Cover
  • ISSN: 1300-0632
  • Yayın Aralığı: Yılda 6 Sayı
  • Yayıncı: TÜBİTAK