Paper 2017/1091

Fairness in an Unfair World: Fair Multiparty Computation from public Bulletin Boards

Arka Rai Choudhuri, Matthew Green, Abhishek Jain, Gabriel Kaptchuk, and Ian Miers


Secure multiparty computation allows mutually distrusting parties to compute a function on their private inputs such that nothing but the function output is revealed. Achieving fairness --- that all parties learn the output or no one does -- is a long studied problem with known impossibility results in the standard model if a majority of parties are dishonest. We present a new model for achieving fairness in MPC against dishonest majority by using public bulletin boards implemented via existing infrastructure such as blockchains or Google's certificate transparency logs. We present both theoretical and practical constructions using either witness encryption or trusted hardware (such as Intel SGX). Unlike previous works that either penalize an aborting party or achieve weaker notions such as $\Delta$-fairness, we achieve complete fairness using existing infrastructure.

Available format(s)
Cryptographic protocols
Publication info
Published elsewhere. Minor revision. ACM CCS 2017
Contact author(s)
achoud @ cs jhu edu
2017-11-10: received
Short URL
Creative Commons Attribution


      author = {Arka Rai Choudhuri and Matthew Green and Abhishek Jain and Gabriel Kaptchuk and Ian Miers},
      title = {Fairness in an Unfair World: Fair Multiparty Computation from public Bulletin Boards},
      howpublished = {Cryptology ePrint Archive, Paper 2017/1091},
      year = {2017},
      note = {\url{}},
      url = {}
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