A non-autonomous IC chaotic oscillator and its application for random bit generation

Truly random number generators based on non‐autonomous continuous‐time chaos

“…It is reported that for a sample size of 419×100 000 bit, the minimum pass rate for each statistical test with the exception of the random excursion (variant) test is approximately 0.975418. It should be noted that as top and bottom distributions have approximately the same density, bit rates of S top and S bottom are equal to the half of external periodical pulse train; hence throughput of S xor is reduced by a factor of two and effectively becomes In our previous work [16], a TRNG based on a non-autonomous chaotic oscillator was considered as well. However, in [16] it was reported that throughput rate of raw bit sequences, which could not pass the randomness tests without Von Neumann post-processing, decreased to f 0 /3.…”

“…It should be noted that as top and bottom distributions have approximately the same density, bit rates of S top and S bottom are equal to the half of external periodical pulse train; hence throughput of S xor is reduced by a factor of two and effectively becomes In our previous work [16], a TRNG based on a non-autonomous chaotic oscillator was considered as well. However, in [16] it was reported that throughput rate of raw bit sequences, which could not pass the randomness tests without Von Neumann post-processing, decreased to f 0 /3. Considering that f xor = f 0 /2 as mentioned above, previous TRNG design [16] results in a sixfold rate reduction in comparison with Regional-TRNG, and the throughput rate of processed sequences after the Von Neumann post-processing approximately becomes f 0 /12.…”

“…Another disadvantage of the previous TRNGs [15][16][17] is the disability to realize necessary offset compensation, which derives from the fact that instead of raw bit sequences, acquired processed sequences can pass the statistical tests thanks to Von Neumann post-processing technique.…”

Security analysis of a chaos-based random number generator for applications in cryptography

“…There are few TRNG designs reported in the literature; however fundamentally four different techniques were mentioned for generating random numbers: amplification of a noise source [2], [3] jittered oscillator sampling [4], [5], [6], discrete-time chaotic maps [7], [8], [9], [10] and continuoustime chaotic oscillators [11], [12], [13].…”

Cryptanalysis of Fridrich's Chaotic Image Encryption

“…Another approach is the discretization of chaotic signals at some stage in the system and using the resulting sequence to modify plaintext, possibly in multiple rounds [Masuda & Aihara, 2002a;Ozoguz et al, 2006]. Some of these schemes use chaotic systems to generate a pseudorandom sequence which is then simply XORed with the plaintext.…”