10 Challenges in Certifying Small-Scale (IoT) Hardware Random Number Generators
181
to issues when considering black-box design philosophy and resource-constrained
devices.
The DESFire EV1 has been found to output biased values from its TRNG, but this
does not necessarily mean that the RNG itself is at fault. Subsequent work with the
EV2 has found no issues with its RNG. Combined with conversations with NXP’s
engineers, this indicates that the issue may instead be in the whitening function
employed to remove bias from the raw TRNG output. The EV1 results highlight two
key issues; the role of black box design in complicating the evaluation process, and
the quality control challenges facing small-scale robust TRNGs. Quantis QRNGs
also require post-processing, as demonstrated by the exceptionally poor results
shown by raw data over a variety of statistical tests. Rigorous testing of RNG with
multiple input and processing dependencies should require results demonstrating
the performance of both raw and processed output of such devices. This will aid in
the identification of implementation errors.
Reliance on Dieharder and NIST SP800-22 cannot continue to the exclusion
of new tests, such as those employed by TestU01. There is a wealth of academic
literature on the subject of statistical tests of randomness and efforts must be made
to identify which of these will provide the next wave of reliable tests of randomness.
Finally, it is important to consider how tests may be evaded by manipulation of
RNG output; future work will focus on how some simple manipulations result in
predictable data that passes current statistical tests of randomness.
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give appropriate
credit to the original author(s) and the source, provide a link to the Creative Commons licence and
indicate if changes were made.
The images or other third party material in this chapter are included in the chapter’s Creative
Commons licence, unless indicated otherwise in a credit line to the material. If material is not
included in the chapter’s Creative Commons licence and your intended use is not permitted by
statutory regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder.
181
to issues when considering black-box design philosophy and resource-constrained
devices.
The DESFire EV1 has been found to output biased values from its TRNG, but this
does not necessarily mean that the RNG itself is at fault. Subsequent work with the
EV2 has found no issues with its RNG. Combined with conversations with NXP’s
engineers, this indicates that the issue may instead be in the whitening function
employed to remove bias from the raw TRNG output. The EV1 results highlight two
key issues; the role of black box design in complicating the evaluation process, and
the quality control challenges facing small-scale robust TRNGs. Quantis QRNGs
also require post-processing, as demonstrated by the exceptionally poor results
shown by raw data over a variety of statistical tests. Rigorous testing of RNG with
multiple input and processing dependencies should require results demonstrating
the performance of both raw and processed output of such devices. This will aid in
the identification of implementation errors.
Reliance on Dieharder and NIST SP800-22 cannot continue to the exclusion
of new tests, such as those employed by TestU01. There is a wealth of academic
literature on the subject of statistical tests of randomness and efforts must be made
to identify which of these will provide the next wave of reliable tests of randomness.
Finally, it is important to consider how tests may be evaded by manipulation of
RNG output; future work will focus on how some simple manipulations result in
predictable data that passes current statistical tests of randomness.
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give appropriate
credit to the original author(s) and the source, provide a link to the Creative Commons licence and
indicate if changes were made.
The images or other third party material in this chapter are included in the chapter’s Creative
Commons licence, unless indicated otherwise in a credit line to the material. If material is not
included in the chapter’s Creative Commons licence and your intended use is not permitted by
statutory regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder.