Safe cryptographic random number generation using untrusted generators

Helena Bruyninckx, Frédéric Lafitte, Dirk Van Heule

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The security of many cryptographic applications relies heavily on the quality of the random numbers used. Therefore, random number generation is one of the most critical primitives for cryptography. This paper focuses on true random number generators (TRNGs) and the analysis of their security requirements. After illustrating issues associated with adversarial influences on TRNGs, we propose a simple method to obtain a secure TRNG based on n TRNGs originating from (potentially) untrusted vendors. The untrusted generators are combined such that as long as one out of the n vendors does not collude with the other vendors, the generator is secure, i.e., the output is unpredictable and uniformly distributed even in the presence of an active attacker. In order to achieve this, we review several choices of functions to be used as combiner. The advantage of our design is that only the (black-box) input-output behavior of the vendor's TRNGs needs to be evaluated. No overhead is introduced by the combiner. The resulting generator offers faultresilience and ease of maintenance.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Communications, ICC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages731-736
Number of pages6
ISBN (Print)9781479920037
DOIs
Publication statusPublished - 2014
Event2014 1st IEEE International Conference on Communications, ICC 2014 - Sydney, NSW, Australia
Duration: 10 Jun 201414 Jun 2014

Publication series

Name2014 IEEE International Conference on Communications, ICC 2014

Conference

Conference2014 1st IEEE International Conference on Communications, ICC 2014
Country/TerritoryAustralia
CitySydney, NSW
Period10/06/1414/06/14

Keywords

  • Attacks on TRNGs
  • Cryptography
  • Fault-tolerance
  • Hardware Trojans
  • Random Number Generator (RNG)
  • Resilient functions

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