Amortizing Secure Computation with Penalties

By Ranjit Kumaresan, Iddo Bentov. 2016

87 citations recorded by the source corpus.

Motivated by the impossibility of achieving fairness in secure computation [Cleve, STOC 1986], recent works study a model of fairness in which an adversarial party that aborts on receiving output is forced to pay a mutually predefined monetary penalty to every other party that did not receive the output. These works show how to design protocols for secure computation with penalties that guarantees that either fairness is guaranteed or that each honest party obtains a monetary penalty from the adversary. Protocols for this task are typically designed in an hybrid model where parties have access to a "claim-or-refund" transaction functionality denote FCR*. In this work, we obtain improvements on the efficiency of these constructions by amortizing the cost over multiple executions of secure computation with penalties. More precisely, for computational security parameter λ, we design a protocol that implements l = poly}(λ) instances of secure computation with penalties where the total number of calls to FCR* is independent of l.

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

In Information Systems

Citations in the corpus

Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contracts (2018, neutral): “Poker is a common benchmark application for blockchain systems and secure multi-party computation called mental poker [11], [43], [42], [6].”
Ouroboros Genesis: Composable Proof-of-Stake Blockchains with Dynamic Availability (2018, background): “For example, a number of recent works [1, 2, 6, 27, 28] describe how blockchain-based cryptocurrencies can be used to obtain a natural notion of fairness in multi-party computation against dishonest majorities; or to allow parties to play games of chance—e.”
Instantaneous Decentralized Poker (2017, methodology): “The ﬁrst secure computation protocols that utilize Bitcoin to guarantee fairness are by Andrychowicz et al. [3, 2] and by Bentov and Kumaresan [6].”
Fairness in an Unfair World: Fair Multiparty Computation from Public Bulletin Boards (2017, background): “Recently, with the popularity of decentralized cryptocurrencies such as Bitcoin, a sequence of works [6, 17, 52, 53] have shown how to implement a fairness-with-penalties model for MPC where adversarial parties who prematurely abort are forced to pay financial fines.”