Limited-birthday distinguishers for hash functions: Collisions beyond the birthday bound can be meaningful

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Abstract

In this article, we investigate the use of limited-birthday distinguishers to the context of hash functions. We first provide a proper understanding of the limited-birthday problem and demonstrate its soundness by using a new security notion Differential Target Collision Resistance (dTCR) that is related to the classical Target Collision Resistance (TCR) notion. We then solve an open problem and close the existing security gap by proving that the best known generic attack proposed at FSE 2010 for the limited-birthday problem is indeed the best possible method. Moreover, we show that almost all known collision attacks are in fact more than just a collision finding algorithm, since the difference mask for the message input is usually fixed. A direct and surprising corollary is that these collision attacks are interesting for cryptanalysis even when their complexity goes beyond the 2n/2 birthday bound and up to the 2n preimage bound, and can be used to derive distinguishers using the limited-birthday problem. Interestingly, cryptanalysts can now search for collision attacks beyond the 2n/2 birthday bound. Finally, we describe a generic algorithm that turns a semi-free-start collision attack on a compression function (even if its complexity is beyond the birthday bound) into a distinguisher on the whole hash function when its internal state is not too wide. To the best of our knowledge, this is the first result that exploits classical semi-free-start collisions on the compression function to exhibit a weakness on the whole hash function. As an application of our findings, we provide distinguishers on reduced or full version of several hash functions, such as RIPEMD-128, SHA-256, Whirlpool, etc. © 2013 Springer-Verlag.

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APA

Iwamoto, M., Peyrin, T., & Sasaki, Y. (2013). Limited-birthday distinguishers for hash functions: Collisions beyond the birthday bound can be meaningful. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8270 LNCS, pp. 504–523). https://doi.org/10.1007/978-3-642-42045-0_26

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