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Compact Round-Optimal Partially-Blind Signatures

  • Olivier Blazy
  • David Pointcheval
  • Damien Vergnaud
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7485)

Abstract

Partially-blind signatures find many applications in the area of anonymity, such as in e-cash or e-voting systems. They extend classical blind signatures, with a signed message composed of two parts: a public one (common to the user and the signer) and a private one (chosen by the user, and blindly signed). The signer cannot link later the message-signature to the initial interaction with the user, among other signatures on messages with the same public part.

This paper presents a one-round partially-blind signature which achieves perfect blindness in the standard model using a Common Reference String, under classical assumptions: CDH and DLin assumptions in symmetric groups, and similar ones in asymmetric groups. This scheme is more efficient than the previous ones: reduced round complexity and communication complexity, but still weaker complexity assumptions. A great advantage is also to end up with a standard Waters signature, which is quite short.

In addition, in all the previous schemes, the public part required a prior agreement between the parties on the public part of the message before running the blind signature protocol. Our protocol does not require such pre-processing: the public part can be chosen by the signer only.

Our scheme even allows multiple messages provided from independent sources to be blindly signed. These messages can either be concatenated or aggregated by the signer, without learning any information about them, before returning the blind signature to the recipient. For the aggregation (addition of the messages), we provide a new result, of independent interest, about the Waters hash function over non binary-alphabets.

Keywords

Signature Scheme Blind Signature Blind Signature Scheme Prior Agreement Common Reference String 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Olivier Blazy
    • 1
  • David Pointcheval
    • 1
  • Damien Vergnaud
    • 1
  1. 1.ENSParisFrance

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