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Pseudorandom functions revisited: the cascade construction and its concrete security

Bellare, M., Canetti, R., Krawczyk, H.·2002·Proceedings of 37th Conference on Foundations of Computer Science

Abstract

Pseudorandom function families are a powerful cryptographic primitive, yielding, in particular simple solutions for the main problems in private key cryptography. Their existence based on general assumptions (namely the existence of one-way functions) has been established. The authors investigate new ways of designing pseudorandom function families. The goal is to find constructions that are both efficient and secure, and thus eventually to bring the benefits of pseudorandom functions to practice. The basic building blocks in the design are certain limited versions of pseudorandom function families, called finite length input pseudorandom function families, for which very efficient realizations exist impractical cryptography. Thus rather than starting from one-way functions, they propose constructions of "full-fledged" pseudorandom function families from these limited ones. In particular they propose the cascade construction, and provide a concrete security analysis which relates the strength of the cascade to that of the underlying finite pseudorandom function family in a precise and quantitative way.

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Metadata

Authors
Bellare, M., Canetti, R., & Krawczyk, H.
Venue
Proceedings of 37th Conference on Foundations of Computer Science
Published
2002-12-24
Pages
514-523
OpenAlex
W2148555789
Cited by
208
References
2
FWCI
5.44
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Pseudorandom functions revisited: the cascade construction and its concrete security. https://app.cochat.ai/papers/10.1109/sfcs.1996.548510
The paper
Bellare, M., Canetti, R., & Krawczyk, H. (2002). Pseudorandom functions revisited: the cascade construction and its concrete security. Proceedings of 37th Conference on Foundations of Computer Science. https://doi.org/10.1109/sfcs.1996.548510

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.