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Accurate Estimation of the Full Differential Distribution for General Feistel Structures

  • Jiageng ChenEmail author
  • Atsuko Miyaji
  • Chunhua Su
  • Je Sen Teh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9589)

Abstract

Statistical cryptanalysis is one of the most powerful tools to analyze symmetric key cryptographic primitives such as block ciphers. One of these attacks, the differential attack has been demonstrated to break a wide range of block ciphers. Block cipher proposals previously obtain a rough estimate of their security margin against differential attacks by counting the number of active S-Box along a differential path. However this method does not take into account the complex clustering effect of multiple differential paths. Analysis under full differential distributions have been studied for some extremely lightweight block ciphers such as KATAN and SIMON, but is still unknown for ciphers with relatively large block sizes. In this paper, we provide a framework to accurately estimate the full differential distribution of General Feistel Structure (GFS) block ciphers with relatively large block sizes. This framework acts as a convenient tool for block cipher designers to determine the security margin of their ciphers against differential attacks. We describe our theoretical model and demonstrate its correctness by performing experimental verification on a toy GFS cipher. We then apply our framework to two concrete GFS ciphers, LBlock and TWINE to derive their full differential distribution by using super computer. Based on the results, we are able to attack 25 rounds of TWINE-128 using a distinguishing attack, which is comparable to the best attack to date. Besides that, we are able to depict a correlation between the hamming weight of an input differential characteristic and the complexity of the attack. Based on the proposed framework, LBlock and TWINE have shown to have 178 and 208-bit security respectively.

Keywords

Differential attack GFS Differential distribution LBlock TWINE 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jiageng Chen
    • 1
    Email author
  • Atsuko Miyaji
    • 2
    • 3
    • 4
  • Chunhua Su
    • 2
  • Je Sen Teh
    • 5
  1. 1.Computer SchoolCentral China Normal UniversityWuhanChina
  2. 2.School of Information ScienceJapan Advanced Institute of Science and TechnologyNomiJapan
  3. 3.Japan Science and Technology Agency (JST) CRESTKawaguchi-shiJapan
  4. 4.Graduate School of EngineeringOsaka UniversityOsakaJapan
  5. 5.School of Computer SciencesUniversiti Sains MalaysiaGelugorMalaysia

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