Efficiently Making Secure Two-Party Computation Fair
Secure two-party computation cannot be fair against malicious adversaries, unless a trusted third party (TTP) or a gradual-release type super-constant round protocol is employed. Existing optimistic fair two-party computation protocols with constant rounds are either too costly to arbitrate (e.g., the TTP may need to re-do almost the whole computation), or require the use of electronic payments. Furthermore, most of the existing solutions were proven secure and fair via a partial simulation, which, we show, may lead to insecurity overall. We propose a new framework for fair and secure two-party computation that can be applied on top of any secure two party computation protocol based on Yao’s garbled circuits and zero-knowledge proofs. We show that our fairness overhead is minimal, compared to all known existing work. Furthermore, our protocol is fair even in terms of the work performed by Alice and Bob. We also prove our protocol is fair and secure simultaneously, through one simulator, which guarantees that our fairness extensions do not leak any private information. Lastly, we ensure that the TTP never learns the inputs or outputs of the computation. Therefore, even if the TTP becomes malicious and causes unfairness by colluding with one party, the security of the underlying protocol is still preserved.
The authors acknowledge the support of TÜBİTAK, the Scientific and Technological Research Council of Turkey, under project number 111E019, and European Union COST Action IC1306.
- 8.R. Cleve: Limits on the security of coin flips when half the processors are faulty. In: STOC (1986)Google Scholar
- 10.Damgård, I.: On Sigma protocols. http://www.daimi.au.dk/~ivan/Sigma.pdf
- 21.Kılınç, H., Küpçü, A.: Efficiently making secure two-party computation fair. Cryptology ePrint Archive, Report 2014/896Google Scholar
- 22.Kiraz, M.S., Schoenmakers, B.: A protocol issue for the malicious case of Yao’s garbled circuit construction. In: Proceedings of 27th Symposium on Information Theory in the Benelux (2006)Google Scholar
- 27.Lindell, Y., Pinkas, B.: Secure multiparty computation for privacy-preserving data mining. J. Privacy Confidentiality 1, 59–98 (2009)Google Scholar
- 38.Yao, A.C.: Protocols for secure computations. In: FOCS (1982)Google Scholar