Low overhead broadcast encryption from multilinear maps

Dan Boneh, Brent Waters, Mark Zhandry

Research output: Chapter in Book/Report/Conference proceedingConference contribution

67 Scopus citations

Abstract

We use multilinear maps to provide a solution to the long-standing problem of public-key broadcast encryption where all parameters in the system are small. In our constructions, ciphertext overhead, private key size, and public key size are all poly-logarithmic in the total number of users. The systems are fully collusion-resistant against any number of colluders. All our systems are based on an O(logN)-way multilinear map to support a broadcast system for N users. We present three constructions based on different types of multilinear maps and providing different security guarantees. Our systems naturally give identity-based broadcast systems with short parameters.

Original languageEnglish (US)
Title of host publicationAdvances in Cryptology, CRYPTO 2014 - 34th Annual Cryptology Conference, Proceedings
PublisherSpringer Verlag
Pages206-223
Number of pages18
EditionPART 1
ISBN (Print)9783662443705
DOIs
StatePublished - Jan 1 2014
Externally publishedYes
Event34rd Annual International Cryptology Conference, CRYPTO 2014 - Santa Barbara, CA, United States
Duration: Aug 17 2014Aug 21 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8616 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other34rd Annual International Cryptology Conference, CRYPTO 2014
CountryUnited States
CitySanta Barbara, CA
Period8/17/148/21/14

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Boneh, D., Waters, B., & Zhandry, M. (2014). Low overhead broadcast encryption from multilinear maps. In Advances in Cryptology, CRYPTO 2014 - 34th Annual Cryptology Conference, Proceedings (PART 1 ed., pp. 206-223). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8616 LNCS, No. PART 1). Springer Verlag. https://doi.org/10.1007/978-3-662-44371-2_12