Abstract
In this paper, we extend the work on purely mathematical Trojan horses initially presented in [15]. This kind of mechanism affects the statistical properties of an infected random number generator (RNG) by making it very sensitive to input entropy. Thereby, when inputs have the correct distribution the Trojan has no effect, but when the distribution becomes biased the Trojan worsens it. Besides its obvious malicious usage, this mechanism can also be applied to devise lightweight health tests for RNGs. Currently, RNG designs are required to implement an early detection mechanism for entropy failure, and this class of Trojan horses is perfect for this job.
Cite
CITATION STYLE
Teşeleanu, G. (2018). Random number generators can be fooled to behave badly. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11149 LNCS, pp. 124–141). Springer Verlag. https://doi.org/10.1007/978-3-030-01950-1_8
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