Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-21040-2_12
Title: A comparison of post-processing techniques for biased random number generators
Authors: Kwok, S.-H.
Ee, Y.-L.
Chew, G.
Zheng, K.
Khoo, K.
Tan, C.-H. 
Keywords: bias
entropy
linear correcting codes
post-processing
random number generators
Issue Date: 2011
Citation: Kwok, S.-H.,Ee, Y.-L.,Chew, G.,Zheng, K.,Khoo, K.,Tan, C.-H. (2011). A comparison of post-processing techniques for biased random number generators. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6633 LNCS : 175-190. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-21040-2_12
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. © 2011 IFIP International Federation for Information Processing.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/111511
ISBN: 9783642210396
ISSN: 03029743
DOI: 10.1007/978-3-642-21040-2_12
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