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Fuzzy Identity-Based Encryption

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Advances in Cryptology – EUROCRYPT 2005 (EUROCRYPT 2005)
Fuzzy Identity-Based Encryption
  • Amit Sahai17 &
  • Brent Waters18 

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 3494))

Included in the following conference series:

  • Annual International Conference on the Theory and Applications of Cryptographic Techniques
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Abstract

We introduce a new type of Identity-Based Encryption (IBE) scheme that we call Fuzzy Identity-Based Encryption. In Fuzzy IBE we view an identity as set of descriptive attributes. A Fuzzy IBE scheme allows for a private key for an identity, ω, to decrypt a ciphertext encrypted with an identity, ω ′, if and only if the identities ω and ω ′ are close to each other as measured by the “set overlap” distance metric. A Fuzzy IBE scheme can be applied to enable encryption using biometric inputs as identities; the error-tolerance property of a Fuzzy IBE scheme is precisely what allows for the use of biometric identities, which inherently will have some noise each time they are sampled. Additionally, we show that Fuzzy-IBE can be used for a type of application that we term “attribute-based encryption”.

In this paper we present two constructions of Fuzzy IBE schemes. Our constructions can be viewed as an Identity-Based Encryption of a message under several attributes that compose a (fuzzy) identity. Our IBE schemes are both error-tolerant and secure against collusion attacks. Additionally, our basic construction does not use random oracles. We prove the security of our schemes under the Selective-ID security model.

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Author information

Authors and Affiliations

  1. University of California, Los Angeles

    Amit Sahai

  2. Stanford University,  

    Brent Waters

Authors
  1. Amit Sahai
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  2. Brent Waters
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Editors and Affiliations

  1. CWI Amsterdam, The Netherlands

    Ronald Cramer

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Sahai, A., Waters, B. (2005). Fuzzy Identity-Based Encryption. In: Cramer, R. (eds) Advances in Cryptology – EUROCRYPT 2005. EUROCRYPT 2005. Lecture Notes in Computer Science, vol 3494. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11426639_27

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  • DOI: https://doi.org/10.1007/11426639_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25910-7

  • Online ISBN: 978-3-540-32055-5

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Keywords

  • Random Oracle
  • Fuzzy Identity
  • Public Parameter
  • Collusion Attack
  • Fuzzy Extractor

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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