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IET Communications
Article . 2025 . Peer-reviewed
License: CC BY NC
Data sources: Crossref
DBLP
Article . 2025
Data sources: DBLP
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A novel distinguishing attack on Rocca

Authors: Nan Liu; Chenhui Jin; Junwei Yu; Jie Guan; Ting Cui;

A novel distinguishing attack on Rocca

Abstract

Abstract The security of an encryption algorithm often hinges on the indistinguishability between ciphertext and random numbers. In block ciphers, pseudorandomness and super‐pseudorandomness are commonly used to depict the indistinguishability between ciphertext and random numbers. Whereas, stream cipher algorithms can be viewed as functions of variables such as the key K, initialization vector IV, and plaintext M. For an ideal stream cipher algorithm, the ciphertext sequences for any two different plaintexts should exhibit good independence across various K‐IV pairs. Consequently, the probability of the two ciphertext sequences being equal or having sufficiently long matching segments should be negligible. This study examines a new type of attack that stems from key commitment attacks. By exploiting the independence between the ciphertext sequences, a novel distinguishing attack against the stream cipher is constructed and applied to the encryption of Rocca. Focusing on the authenticated encryption algorithm Rocca, a guess‐and‐determine method is employed to demonstrate that for any plaintext and two different sets of , another plaintext can be found with a time complexity , resulting in identical ciphertext sequences except for the initial bits. Furthermore, it is proved that under chosen plaintext conditions, the time complexity of this distinguishing attack is . These findings imply that Rocca does not offer 256‐bit security against such distinguishing attacks, providing valuable insights into the design of round update functions for stream ciphers like Rocca.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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