Generation, evaluation and comparison of DNA-based (pseudo) random sequences

The object of this study is the methods and algorithms for computing, evaluating, and comparing DNA-based pseudorandom sequences (PRSs) and random sequences (RSs). This paper addresses the task of extracting (P)RSs with the required stochastic and statistical properties from a DNA noise source, experimentally validating these properties in compliance with the requirements of current international standards, as well as evaluating and comparing the sequences obtained for different DNA samples. The results are the developed algorithms for generating DNA-based (P)RSs, improved algorithms for their comparison, and proposals for the process of evaluating their properties. For the output of implemented algorithms, more than 96 % of the bit streams of sequences successfully pass all statistical tests, and the entropy per bit for RSs is close to 1. A special feature of the developed generation algorithms is the use of validated conditioning component – block cipher in CTR mode – which explains the possibility of obtaining unique random data with required properties. The peculiarity of the proposed evaluation algorithm is the complexity of the tests and checks used: complete assessment of statistical properties and entropy values. Special features of the improved comparison algorithms are resource saving and the ability to evaluate much larger data sets. This is due to the use of structures and data types that are better in terms of memory usage and flexible cryptographic primitives with different modes. The results could be practically applied to constructs where randomness is required: for computing the keys and system-wide parameters, when performing transformations as part of hash functions, while obtaining sequences of an arbitrary alphabet, in zero-knowledge protocols, etc.