Fully Homomorphic Encryption from Ring-LWE and Security for Key Dependent Messages View Full Text


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Chapter Info

DATE

2011

AUTHORS

Zvika Brakerski , Vinod Vaikuntanathan

ABSTRACT

We present a somewhat homomorphic encryption scheme that is both very simple to describe and analyze, and whose security (quantumly) reduces to the worst-case hardness of problems on ideal lattices. We then transform it into a fully homomorphic encryption scheme using standard “squashing” and “bootstrapping” techniques introduced by Gentry (STOC 2009). One of the obstacles in going from “somewhat” to full homomorphism is the requirement that the somewhat homomorphic scheme be circular secure, namely, the scheme can be used to securely encrypt its own secret key. For all known somewhat homomorphic encryption schemes, this requirement was not known to be achievable under any cryptographic assumption, and had to be explicitly assumed. We take a step forward towards removing this additional assumption by proving that our scheme is in fact secure when encrypting polynomial functions of the secret key. Our scheme is based on the ring learning with errors (RLWE) assumption that was recently introduced by Lyubashevsky, Peikert and Regev (Eurocrypt 2010). The RLWE assumption is reducible to worst-case problems on ideal lattices, and allows us to completely abstract out the lattice interpretation, resulting in an extremely simple scheme. For example, our secret key is s, and our public key is (a,b = as + 2e), where s,a,e are all degree (n − 1) integer polynomials whose coefficients are independently drawn from easy to sample distributions. More... »

PAGES

505-524

References to SciGraph publications

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  • Book

    TITLE

    Advances in Cryptology – CRYPTO 2011

    ISBN

    978-3-642-22791-2
    978-3-642-22792-9

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-22792-9_29

    DOI

    http://dx.doi.org/10.1007/978-3-642-22792-9_29

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1049197805


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