Visible to the public Experimental Study of Secure PRNG for Q-trits Quantum Cryptography Protocols

TitleExperimental Study of Secure PRNG for Q-trits Quantum Cryptography Protocols
Publication TypeConference Paper
Year of Publication2020
AuthorsGnatyuk, Sergiy, Okhrimenko, Tetiana, Azarenko, Olena, Fesenko, Andriy, Berdibayev, Rat
Conference Name2020 IEEE 11th International Conference on Dependable Systems, Services and Technologies (DESSERT)
Keywordscomposability, compositionality, cybersecurity, deterministic protocol, NIST STS, PRNG, pseudorandom numbers, pubcrawl, Q-trit, quantum cryptography, statistical stability, theoretical cryptography
AbstractQuantum cryptography doesn't depend on computational capabilities of intruders; it uses inviolability of quantum physics postulates (postulate of measurement, no-cloning theorem, uncertainty principle). Some quantum key distribution protocols have absolute (theoretical and informational) stability, but quantum secure direct communication (deterministic) protocols have only asymptotic stability. For a whole class of methods to ensure Q-trit deterministic quantum cryptography protocols stability, reliable trit generation method is required. In this paper, authors have developed a high-speed and secure pseudorandom number (PRN) generation method. This method includes the following steps: initialization of the internal state vector and direct PRN generation. Based on this method TriGen v.2.0 pseudo-random number generator (PRNG) was developed and studied in practice. Therefore, analysing the results of study it can be concluded following: 1) Proposed Q-trit PRNG is better then standard C ++ PRNG and can be used on practice for critical applications; 2) NIST STS technique cannot be used to evaluate the quality (statistical stability) of the Q-trit PRNG and formed trit sequences; 3) TritSTS 2020 technique is suitable for evaluating Q-trit PRNG and trit sequences quality. A future research study can be related to developing a fully-functional version of TritSTS technique and software tool.
DOI10.1109/DESSERT50317.2020.9125007
Citation Keygnatyuk_experimental_2020