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

By Arka Rai Choudhuri, Matthew D. Green, Abhishek Jain, Gabriel Kaptchuk, Ian Miers. 2017

133 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ian Miers
2. Matthew D. Green
3. Shui Yu
4. Liehuang Zhu
5. Keke Gai
6. Gabriel Kaptchuk
7. Jinnan Guo
8. George Danezis

In Information Systems

1. Matthew D. Green
2. Shui Yu
3. Liehuang Zhu
4. Keke Gai
5. Ian Miers
6. Gabriel Kaptchuk
7. Jinnan Guo
8. George Danezis

Citations in the corpus

• Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contracts (2018, neutral): “In permissionless blockchains, especially proof-of-work based ones, however, only a weaker notion of security can be achieved, as acknowledged in [23].”
• Blockchain Meets Cloud Computing: A Survey (2020, background): “Fairness [116] also was provided by smart contract in the form of time commitment.”
• Giving State to the Stateless: Augmenting Trustworthy Computation with Ledgers (2019, methodology): “This model has been previously investigated independently in a more limited fashion by other works, notably in the context of fair multiparty computation [22], [29].”