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A Comparison of Post-Processing Techniques for Biased Random Number Generators

Abstract : In this paper, we study and compare two popular methods for post-processing random number generators: linear and Von Neumann compression. We show that linear compression can achieve much better throughput than Von Neumann compression, while achieving practically good level of security. We also introduce a concept known as the adversary bias which measures how accurately an adversary can guess the output of a random number generator, e.g. through a trapdoor or a bad RNG design. Then we prove that linear compression performs much better than Von Neumann compression when correcting adversary bias. Finally, we discuss on good ways to implement this linear compression in hardware and give a field-programmable gate array (FPGA) implementation to provide resource utilization estimates.
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Siew-Hwee Kwok, Yen-Ling Ee, Guanhan Chew, Kanghong Zheng, Khoongming Khoo, et al.. A Comparison of Post-Processing Techniques for Biased Random Number Generators. 5th Workshop on Information Security Theory and Practices (WISTP), Jun 2011, Heraklion, Crete, Greece. pp.175-190, ⟨10.1007/978-3-642-21040-2_12⟩. ⟨hal-01573305⟩



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