Cryptanalysis of the full Spritz stream cipher

Subhadeep Banik, Takanori Isobe

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Spritz is a stream cipher proposed by Rivest and Schuldt at the rump session of CRYPTO 2014. It is intended to be a replacement of the popular RC4 stream cipher. In this paper we propose distinguishing attacks on the full Spritz, based on a short-term bias in the first two bytes of a keystream and a long-term bias in the first two bytes of every cycle of N keystream bytes, where N is the size of the internal permutation. Our attacks are able to distinguish a keystream of the full Spritz from a random sequence with samples of first two bytes produced by 244.8 multiple key-IV pairs or 260.8 keystream bytes produced by a single key- IV pair. These biases are also useful in the event of plaintext recovery in a broadcast attack. In the second part of the paper, we look at a state recovery attack on Spritz, in a special situation when the cipher enters a class of weak states. We determine the probability of encountering such a state, and demonstrate a state recovery algorithm that betters the 21400 step algorithm of Ankele et al. at Latincrypt 2015.
Original languageEnglish
Title of host publicationRevised Selected Papers of the 23rd International Conference on Fast Software Encryption (FSE 2016)
PublisherSpringer
Publication date2016
Pages63-77
ISBN (Print)978-3-662-52992-8
ISBN (Electronic)978-3-662-52993-5
DOIs
Publication statusPublished - 2016
Event23rd International Conference on Fast Software Encryption (FSE 2016) - Bochum, Germany
Duration: 20 Mar 201623 Mar 2016
Conference number: 23
https://fse.rub.de/

Conference

Conference23rd International Conference on Fast Software Encryption (FSE 2016)
Number23
Country/TerritoryGermany
CityBochum
Period20/03/201623/03/2016
Internet address
SeriesLecture Notes in Computer Science
Volume9783
ISSN0302-9743

Keywords

  • RC4
  • Spritz
  • Stream cipher
  • Short-term bias
  • Long-term bias
  • Distinguishing attack
  • Plaintext recovery attack
  • State recovery attack

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