Order and chaos download 4.2.0 pc
Schindler W, Killmann W (2003) Evaluation criteria for true (physical) random number generators used in cryptographic applications. Sanguinetti B, Martin A, Zbinden H, Gisin N (2014) Quantum random number generation on a mobile phone. Oteo JA, Ros J (2007) Double precision errors in the logistic map: statistical study and dynamical interpretation. Marsaglia G (1995) The marsaglia random number cdrom including the diehard battery of tests of randomness. Keuninckx L, Soriano MC, Fischer I, Mirasso CR, Nguimdo RM, der Sande GV (2017) Encryption key distribution via chaos synchronization. IEICE Trans Fund Electron Commun Comput Sci E100.A (1):158–166.
#Order and chaos download 4.2.0 pc generator
Kanak A, Ergun S (2017) A practical biometric random number generator for mobile security applications. Gan Z, Chai X, Yuan K, Lu Y (2018) A novel image encryption algorithm based on lft based s-boxes and chaos. In: Proceedings of the 2013 ACM SIGSAC conference on computer & communications security, CCS ’13. Springer, Berlin, pp 114–120ĭodis Y, Pointcheval D, Ruhault S, Vergniaud D, Wichs D (2013) Security analysis of pseudo-random number generators with input: /dev/random is not robust. In: Desmedt YG (ed) Advances in cryptology - CRYPTO ’94. Springer, Berlin, pp 29–42ĭavis D, Ihaka R, Fenstermacher P (1994) Cryptographic randomness from air turbulence in disk drives. Ĭoron JS (1999) On the security of random sources. Ĭicek I, Pusane AE, Dundar G (2014) A novel design method for discrete time chaos based true random number generators. Springer, Berlin, pp 179–189Ĭarter J, Wegman MN (1979) Universal classes of hash functions. In: Jøsang A, Maseng T, Knapskog SJ (eds) Identity and privacy in the internet age. īouda J, Krhovjak J, Matyas V, Svenda P (2009) Towards true random number generation in mobile environments. rep., National Institute of Standards and Technology.
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īassham LE, Rukhin AL, Soto J, Nechvatal JR, Smid ME, Leigh SD, Levenson M, Vangel M, Heckert NA, Banks DL (2010) A statistical test suite for random and pseudorandom number generators for cryptographic applications. Image/Video Indexing and RetrievalĪltaf M, Ahmad A, Khan FA, Uddin Z, Yang X (2018) Computationally efficient selective video encryption with chaos based block cipher.
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Springer, Berlin, pp 355–377Īksoy S, Haralick RM (2001) Feature normalization and likelihood-based similarity measures for image retrieval. In addition, the generator is designed to support parallel processing, thus allowing its performance to scale according to the mobile device’s multicore architecture.Īddabbo T, Fort A, Rocchi S, Vignoli V (2009) Chaos based generation of true random bits. Results indicate that the proposed true random number generator is able to generate statistically secure true random number sequences faster than existing mobile-based generators. Next, the generator’s forward and backward security is analysed. The proposed generator is evaluated in two phases: first, statistical test suites are executed to identify statistical defects. These goals are achieved by using the inherent properties of a chaotic system to amplify entropy extracted from the captured images. The proposed generator implements a chaos-based postprocessing algorithm that eliminates statistical defects and increases its throughput. Although similar generators exist, they suffer from poor performance and a lack of proper security evaluation.
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This paper introduces a new generator for the Android mobile platform based on images captured by a built-in camera. To overcome this drawback, generators have been designed to extract unpredictability from devices such as computer processing units or microphones. However, they are slower than their pseudorandom counterparts because they need to extract entropy from physical phenomenon.
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True random number generators are used in high security applications such as cryptography where non-determinism is required.