Öz
The generation of random numbers is crucial for various applications, including cryptography, simulation, sampling, and statistical analysis. Cryptography utilizes random numbers to secure communication through the generation of encryption keys, thereby safeguarding sensitive information from unauthorized access. This study aims to evaluate the randomness and suitability of the Permuted Congruential Generator (PCG) algorithm for cryptography applications, through testing its generated random numbers using the National Institute of Standards and Technology (NIST) statistical tests. A novel method is proposed for generating 100 million bits using the PCG algorithm. The generated random numbers are then subjected to NIST testing. The results indicate that the PCG-generated random numbers pass most relevant statistical tests and comply with the standards of randomness necessary for cryptography. In conclusion, the PCG algorithm is demonstrated to be a robust, dependable, and appropriate random number generator for cryptography and other applications requiring random numbers.
Anahtar Kelimeler
Kaynakça
- Akashi N, Nakajima K, Shibayama M, Kuniyoshi Y. A mechanical true random number generator. New J Phys 2022; 24(1): 249-252.
- Basharat I, Azam F, Muzaffar AW. Database security and encryption: A survey study. Int J Comput Appl 2012; 47(12): 28-34.
- Bouillaguet C, Martinez F, Sauvage J. Practical seed-recovery for the PCG pseudo-random number generator. IACR Trans Symmetric Cryptol 2020; 2020(3): 175-196.
- Dong L, Chen K. Cryptographic protocol. Security analysis based on trusted freshness. Springer, 2012.
- Johnston D. Random number generators—principles and practices. In Random Number Generators—Principles and Practices. De Gruyter Press, 2018.
- Kohlbrenner P, Gaj K. An embedded true random number generator for FPGAs. Proc ACM SIGDA Int Symp Field Program Gate Arrays 2004; 71–78.
- Naik RB, Singh U. A review on applications of chaotic maps in pseudo-random number generators and encryption. Ann Data Sci 2022; 1-26.
- O’Neill ME. PCG: A family of simple fast space-efficient statistically good algorithms for random number generation. ACM Trans Math Softw, 2014.
- Paar C, Pelzl J. Understanding cryptography: a textbook for students and practitioners. Springer Sci Bus Media, 2009.
- Panda M. Performance analysis of encryption algorithms for security. In Proc Int Conf Signal Process Commun Power Embed Syst (SCOPES). IEEE, 2016; 278-284.
- Pareschi F, Rovatti R, Setti G. Second-level NIST randomness tests for improving test reliability. In Proc IEEE Int Symp Circuits Syst (ISCAS) 2007; 1437-1440.
- Patnala TR, Jayanthi D, Majji S, Valleti M, Kothapalli S, Karanam SR. A modernistic way for key generation for highly secure data transfer in ASIC design flow. In Proc Int Conf Adv Comput Commun Syst (ICACCS). IEEE, 2020; 892-897.
- Ponuma R, Amutha R. Compressive sensing based image compression-encryption using novel 1D-chaotic map. Multimed Tools Appl 2018; 77: 19209-19234.
- Sharma S, Gupta Y. Study on cryptography and techniques. Int J Sci Res Comput Sci Eng Inf Technol 2017; 2(1): 249–252.
- Soto J. Statistical testing of random number generators. In Proc Natl Inf Syst Secur Conf 1999; 12.
- Thambiraja E, Ramesh G, Umarani DR. A survey on various most common encryption techniques. Int J Adv Res Comput Sci Softw Eng 2012; 2(7).
- Zaman JKMS, Ghosh R. Review on fifteen statistical tests proposed by NIST. J Theor Phys Cryptogr 2012; 1: 18-31.
- Zaru A, Khan M. General summary of cryptography. J Eng Res Appl 2018; 8(02): 68-71.
- Zhu S, Ma Y, Lin J, Zhuang J, Jing J. More powerful and reliable second-level statistical randomness tests for NIST SP 800-22. In Adv Cryptol – ASIACRYPT. Springer 2016; 307-329.
Öz
Rastgele sayıların üretilmesi kriptografi, simülasyon, örnekleme ve istatistiksel analiz gibi çeşitli uygulamalar için çok önemlidir. Kriptografi, şifreleme anahtarlarının oluşturulması yoluyla iletişimi güvence altına almak için rastgele sayıları kullanır ve böylece hassas bilgileri yetkisiz erişime karşı korur. Bu çalışma, Permuted Congruential Generator (PCG) algoritmasının kriptografi uygulamaları için rastgeleliğini ve uygunluğunu, üretilen rastgele sayıları Ulusal Standartlar ve Teknoloji Enstitüsü (NIST) istatistiksel testlerini kullanarak test ederek değerlendirmeyi amaçlamaktadır. PCG algoritmasını kullanarak 100 milyon bit üretmek için yeni bir yöntem önerilmiştir. Üretilen rastgele sayılar daha sonra NIST testine tabi tutulmuştur. Sonuçlar, PCG tarafından üretilen rastgele sayıların ilgili istatistiksel testlerin çoğunu geçtiğini ve kriptografi için gerekli rastgelelik standartlarına uygun olduğunu göstermektedir. Sonuç olarak, PCG algoritmasının kriptografi ve rastgele sayı gerektiren diğer uygulamalar için sağlam, güvenilir ve uygun bir rastgele sayı üreteci olduğu gösterilmiştir.
Anahtar Kelimeler
Kaynakça
- Akashi N, Nakajima K, Shibayama M, Kuniyoshi Y. A mechanical true random number generator. New J Phys 2022; 24(1): 249-252.
- Basharat I, Azam F, Muzaffar AW. Database security and encryption: A survey study. Int J Comput Appl 2012; 47(12): 28-34.
- Bouillaguet C, Martinez F, Sauvage J. Practical seed-recovery for the PCG pseudo-random number generator. IACR Trans Symmetric Cryptol 2020; 2020(3): 175-196.
- Dong L, Chen K. Cryptographic protocol. Security analysis based on trusted freshness. Springer, 2012.
- Johnston D. Random number generators—principles and practices. In Random Number Generators—Principles and Practices. De Gruyter Press, 2018.
- Kohlbrenner P, Gaj K. An embedded true random number generator for FPGAs. Proc ACM SIGDA Int Symp Field Program Gate Arrays 2004; 71–78.
- Naik RB, Singh U. A review on applications of chaotic maps in pseudo-random number generators and encryption. Ann Data Sci 2022; 1-26.
- O’Neill ME. PCG: A family of simple fast space-efficient statistically good algorithms for random number generation. ACM Trans Math Softw, 2014.
- Paar C, Pelzl J. Understanding cryptography: a textbook for students and practitioners. Springer Sci Bus Media, 2009.
- Panda M. Performance analysis of encryption algorithms for security. In Proc Int Conf Signal Process Commun Power Embed Syst (SCOPES). IEEE, 2016; 278-284.
- Pareschi F, Rovatti R, Setti G. Second-level NIST randomness tests for improving test reliability. In Proc IEEE Int Symp Circuits Syst (ISCAS) 2007; 1437-1440.
- Patnala TR, Jayanthi D, Majji S, Valleti M, Kothapalli S, Karanam SR. A modernistic way for key generation for highly secure data transfer in ASIC design flow. In Proc Int Conf Adv Comput Commun Syst (ICACCS). IEEE, 2020; 892-897.
- Ponuma R, Amutha R. Compressive sensing based image compression-encryption using novel 1D-chaotic map. Multimed Tools Appl 2018; 77: 19209-19234.
- Sharma S, Gupta Y. Study on cryptography and techniques. Int J Sci Res Comput Sci Eng Inf Technol 2017; 2(1): 249–252.
- Soto J. Statistical testing of random number generators. In Proc Natl Inf Syst Secur Conf 1999; 12.
- Thambiraja E, Ramesh G, Umarani DR. A survey on various most common encryption techniques. Int J Adv Res Comput Sci Softw Eng 2012; 2(7).
- Zaman JKMS, Ghosh R. Review on fifteen statistical tests proposed by NIST. J Theor Phys Cryptogr 2012; 1: 18-31.
- Zaru A, Khan M. General summary of cryptography. J Eng Res Appl 2018; 8(02): 68-71.
- Zhu S, Ma Y, Lin J, Zhuang J, Jing J. More powerful and reliable second-level statistical randomness tests for NIST SP 800-22. In Adv Cryptol – ASIACRYPT. Springer 2016; 307-329.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Bilgi Güvenliği ve Kriptoloji |
| Bölüm | TJST |
| Yazarlar | |
| Yayımlanma Tarihi | 27 Mart 2025 |
| Gönderilme Tarihi | 5 Ağustos 2024 |
| Kabul Tarihi | 20 Kasım 2024 |
| Yayımlandığı Sayı | Yıl 2025 Cilt: 20 Sayı: 1 |
