Correlated Pseudorandomness from Expand-Accumulate Codes

Authors
  • E. Boyle
  • G. Couteau
  • N. Gilboa
  • Y. Ishai
Publication date 2022
Host editors
  • Y. Dodis
  • T. Shrimpton
Book title Advances in Cryptology – CRYPTO 2022
Book subtitle 42nd Annual International Cryptology Conference, CRYPTO 2022, Santa Barbara, CA, USA, August 15–18, 2022 : proceedings
ISBN
  • 9783031159787
ISBN (electronic)
  • 9783031159794
Series Lecture Notes in Computer Science
Event 42nd Annual International Cryptology Conference, CRYPTO 2022
Pages (from-to) 603-633
Number of pages 31
Publisher Cham: Springer
Organisations
  • Faculty of Science (FNWI) - Informatics Institute (IVI)
Abstract

A pseudorandom correlation generator (PCG) is a recent tool for securely generating useful sources of correlated randomness, such as random oblivious transfers (OT) and vector oblivious linear evaluations (VOLE), with low communication cost. We introduce a simple new design for PCGs based on so-called expand-accumulate codes, which first apply a sparse random expander graph to replicate each message entry, and then accumulate the entries by computing the sum of each prefix. Our design offers the following advantages compared to state-of-the-art PCG constructions: Competitive concrete efficiency backed by provable security against relevant classes of attacks;An offline-online mode that combines near-optimal cache-friendliness with simple parallelization;Concretely efficient extensions to pseudorandom correlation functions, which enable incremental generation of new correlation instances on demand, and to new kinds of correlated randomness that include circuit-dependent correlations. To further improve the concrete computational cost, we propose a method for speeding up a full-domain evaluation of a puncturable pseudorandom function (PPRF). This is independently motivated by other cryptographic applications of PPRFs.

Document type Conference contribution
Language English
Published at https://doi.org/10.1007/978-3-031-15979-4_21
Other links https://www.scopus.com/pages/publications/85141734471
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