Skip to main navigation Skip to search Skip to main content

Notice of Removal: DLDL: A Lightweight Hash Function Based on Double Linear Diffusion Layer

  • Beihang University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Due to the property of constrained resources in embedded devices, lightweight cryptography emerges in this environment. In this paper, a novel lightweight hash function DLDL is proposed, which is based on the sponge construction and PRESENT-like internal primitive. A small scale linear diffusion layer is added in PRESENT to increase the confusion of linear diffusion layer with few hardware cost. Besides, the paper analyzes the security of holistic and internal primitive and designs the multiplexing scheme of 3-bit S-box, moreover, it provides the hardware implementation of DLDL and compares with other analogous lightweight hash functions. The experimental results indicate that DLDL is close to PHOTON but a little lower than SPONGENT in terms of security. However, DLDL evidently achieves less cost than SPONGENT and PHOTON in hardware implementation.

Original languageEnglish
Title of host publicationProceedings of 2018 International Computers, Signals and Systems Conference, ICOMSSC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages848-852
Number of pages5
ISBN (Electronic)9781538667514
DOIs
StatePublished - Sep 2018
Event2018 International Computers, Signals and Systems Conference, ICOMSSC 2018 - Dalian, China
Duration: 28 Sep 201830 Sep 2018

Publication series

NameProceedings of 2018 International Computers, Signals and Systems Conference, ICOMSSC 2018

Conference

Conference2018 International Computers, Signals and Systems Conference, ICOMSSC 2018
Country/TerritoryChina
CityDalian
Period28/09/1830/09/18

Keywords

  • PRESENT
  • hash function
  • lightweight
  • linear diffusion layer

Fingerprint

Dive into the research topics of 'Notice of Removal: DLDL: A Lightweight Hash Function Based on Double Linear Diffusion Layer'. Together they form a unique fingerprint.

Cite this