A chaos-based keyed hash function based on fixed point representation

dc.contributor.authorSen Teh, Je
dc.contributor.authorTan, Kaijun
dc.contributor.authorAlawida, Moatsum
dc.date.accessioned2022-06-15T12:51:31Z
dc.date.accessioned2023-08-19T08:18:32Z
dc.date.available2022-06-15T12:51:31Z
dc.date.available2023-08-19T08:18:32Z
dc.date.issued2019-05
dc.description.abstractChaotic maps are used in the design of hash functions due to their characteristics that are analogous to cryptographic requirements. However, these maps are commonly implemented using floating point representation which has high computational complexity. They also suffer from interoperability problems and are not easy to analyse from the binary point of view. These drawbacks lead to a lack of acceptance of chaos-based cryptography for practical use. This paper overcomes these problems by introducing a chaos-based hash function implemented using fixed point representation which computes digital chaotic maps using integers. Its design is based on the Merkle–Damgård construction and the generalised Feistel structure for strong security justifications. Security evaluation indicates that the proposed hash function has near-perfect statistical properties which include diffusion, confusion, collision resistance and distribution. The proposed hash function also surpasses existing chaos-based hash functions in terms of performance, making it a viable hash function for practical implementation.en_US
dc.identifier.citationTeh, J. S., Tan, K., & Alawida, M. (2019). A chaos-based keyed hash function based on fixed point representation. Cluster Computing, 22(2), 649-660.en_US
dc.identifier.doihttps://doi.org/10.1007/s10586-018-2870-z
dc.identifier.urihttps://edms.wexl.in/handle/1/3732
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectCryptographyen_US
dc.subjectChaos theoryen_US
dc.subjectHash functionen_US
dc.subjectFixed point arithmeticen_US
dc.subjectchaen_US
dc.titleA chaos-based keyed hash function based on fixed point representationen_US
dc.title.alternativeCluster Computingen_US
dc.typeArticleen_US

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