Skip to main content
Springer Nature Link
Log in
Menu
Find a journal Publish with us Track your research
Search
Saved research
Cart
  1. Home
  2. Journal of Cryptology
  3. Article

Fast Correlation Attacks on the Summation Generator

  • Published: 01 July 2000
  • Volume 13, pages 245–262, (2000)
  • Cite this article
Download PDF
Save article
View saved research
Journal of Cryptology Aims and scope Submit manuscript
Fast Correlation Attacks on the Summation Generator
Download PDF
  • Jovan Dj. Golic1,
  • Mahmoud Salmasizadeh2 &
  • Ed Dawson3 
  • 445 Accesses

  • 24 Citations

  • Explore all metrics

Abstract.

The linear sequential circuit approximation method for combiners with memory is used to find mutually correlated linear transforms of the input and output sequences in the well-known summation generator with any number of inputs. It is shown that the determined correlation coefficient is large enough for applying a fast correlation attack to the output sequence to reconstruct the initial states of the input linear feedback shift registers. The proposed attack is based on iterative probabilistic decoding and appropriately generated low-weight parity-checks. The required output sequence length and the computational complexity are both derived. Successful experimental results for the summation generators with three and five inputs are obtained.

Article PDF

Download to read the full article text

Similar content being viewed by others

A computational approach for the study of linear complexity of shrunken sequences

Article Open access 22 July 2025

Impacts of Architectural Enhancements on Sequential Recommendation Models

Chapter © 2023

Recursion Polynomials of Unfolded Sequences

Chapter © 2021

Explore related subjects

Discover the latest articles, books and news in related subjects, suggested using machine learning.
  • Algorithms
  • Digital and Analog Signal Processing
  • Logic gates
  • Interspersed repetitive sequences
  • Register-Transfer-Level Implementation
  • Special Functions
  • Lightweight Cryptographic Algorithms for Secure IoT Systems

Author information

Authors and Affiliations

  1. School of Electrical Engineering, University of Belgrade, Bulevar Revolucije 73, 10001, Belgrade, Yugoslavia

    Jovan Dj. Golic

  2. Electronic Research Centre, Sharif University of Technology, P.O. Box 11365-8639, Tehran, Iran

    Mahmoud Salmasizadeh

  3. Information Security Research Centre, Queensland University of Technology, Queensland 4001, GPO Box 2434, Brisbane, Australia

    Ed Dawson

Authors
  1. Jovan Dj. Golic
    View author publications

    Search author on:PubMed Google Scholar

  2. Mahmoud Salmasizadeh
    View author publications

    Search author on:PubMed Google Scholar

  3. Ed Dawson
    View author publications

    Search author on:PubMed Google Scholar

Additional information

Received 13 December 1996 and revised 7 October 1998

Rights and permissions

Reprints and permissions

About this article

Cite this article

Golic, J., Salmasizadeh, M. & Dawson, E. Fast Correlation Attacks on the Summation Generator . J. Cryptology 13, 245–262 (2000). https://doi.org/10.1007/s001459910009

Download citation

  • Published: 01 July 2000

  • Issue date: March 2000

  • DOI: https://doi.org/10.1007/s001459910009

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Key words. Summation generator, Correlation attacks, Linear approximations, Correlation coefficients, Parity-checks.

Advertisement

Search

Navigation

  • Find a journal
  • Publish with us
  • Track your research

Footer Navigation

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Journal finder
  • Publish your research
  • Language editing
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our brands

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Discover

Corporate Navigation

  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support
  • Legal notice
  • Cancel contracts here

162.0.217.198

Not affiliated

Springer Nature

© 2026 Springer Nature