Literature DB >> 32230946

Cryptographically Secure Pseudo-Random Number Generator IP-Core Based on SHA2 Algorithm.

Luca Baldanzi1, Luca Crocetti1, Francesco Falaschi1, Matteo Bertolucci1, Jacopo Belli1, Luca Fanucci1, Sergio Saponara1.   

Abstract

In the context of growing the adoption of advanced sensors and systems for active vehicle safety and driver assistance, an increasingly important issue is the security of the information exchanged between the different sub-systems of the vehicle. Random number generation is crucial in modern encryption and security applications as it is a critical task from the point of view of the robustness of the security chain. Random numbers are in fact used to generate the encryption keys to be used for ciphers. Consequently, any weakness in the key generation process can potentially leak information that can be used to breach even the strongest cipher. This paper presents the architecture of a high performance Random Number Generator (RNG) IP-core, in particular a Cryptographically Secure Pseudo-Random Number Generator (CSPRNG) IP-core, a digital hardware accelerator for random numbers generation which can be employed for cryptographically secure applications. The specifications used to develop the proposed project were derived from dedicated literature and standards. Subsequently, specific architecture optimizations were studied to achieve better timing performance and very high throughput values. The IP-core has been validated thanks to the official NIST Statistical Test Suite, in order to evaluate the degree of randomness of the numbers generated in output. Finally the CSPRNG IP-core has been characterized on relevant Field Programmable Gate Array (FPGA) and ASIC standard-cell technologies.

Entities:  

Keywords:  ASIC standard-cell; FPGA; HW accelerator; SHA2; autonomous driving; cyber security; intelligent sensors; on-chip random number generator (RNG)

Year:  2020        PMID: 32230946     DOI: 10.3390/s20071869

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  2 in total

Review 1.  Design and Test of an Integrated Random Number Generator with All-Digital Entropy Source.

Authors:  Luca Crocetti; Stefano Di Matteo; Pietro Nannipieri; Luca Fanucci; Sergio Saponara
Journal:  Entropy (Basel)       Date:  2022-01-18       Impact factor: 2.524

2.  A Cybersecure P300-Based Brain-to-Computer Interface against Noise-Based and Fake P300 Cyberattacks.

Authors:  Giovanni Mezzina; Valerio F Annese; Daniela De Venuto
Journal:  Sensors (Basel)       Date:  2021-12-10       Impact factor: 3.576

  2 in total

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