Secure Computation Without Authentication

  • Boaz Barak
  • Ran Canetti
  • Yehuda Lindell
  • Rafael Pass
  • Tal Rabin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3621)


In the setting of secure multiparty computation, a set of parties wish to jointly compute some function of their inputs. Such a computation must preserve certain security properties, like privacy and correctness, even if some of the participating parties or an external adversary collude to attack the honest parties. Until this paper, all protocols for general secure computation assumed that the parties can communicate reliably via authenticated channels. In this paper, we consider the feasibility of secure computation without any setup assumption.

We consider a completely unauthenticated setting, where all messages sent by the parties may be tampered with and modified by the adversary (without the honest parties being able to detect this fact). In this model, it is not possible to achieve the same level of security as in the authenticated-channel setting. Nevertheless, we show that meaningful security guarantees can be provided. In particular, we define a relaxed notion of what it means to “securely compute” a function in the unauthenticated setting. Then, we construct protocols for securely realizing any functionality in the stand-alone model, with no setup assumptions whatsoever. In addition, we construct universally composable protocols for securely realizing any functionality in the common reference string model (while still in an unauthenticated network). We also show that our protocols can be used to provide conceptually simple and unified solutions to a number of problems that were studied separately in the past, including password-based authenticated key exchange and non-malleable commitments.


Signature Scheme Secure Computation Honest Party Security Guarantee Secure Multiparty Computation 
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 2005

Authors and Affiliations

  • Boaz Barak
    • 1
  • Ran Canetti
    • 2
  • Yehuda Lindell
    • 3
  • Rafael Pass
    • 4
  • Tal Rabin
    • 2
  1. 1.IAS 
  2. 2.IBM Research 
  3. 3.Bar-Ilan UniversityIsrael
  4. 4.MIT 

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