On the Efficiency of Multi-party Contract Signing Protocols

Draper-Gil, Gerard; Ferrer-Gomila, Josep-Lluís; Hinarejos, M. Francisca; Zhou, Jianying

doi:10.1007/978-3-319-23318-5_13

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…Mauw and Radomirović [70] uses different approach for MPCS which uses skeletal graph. Even though MPCS is considered mostly in complete topologies, Draper et al [36] also analyze the other topologies for contract signing and informally finds an optimal bound for every topology which is O(n 2 ), supporting our optimality claim.…”

Section: Num Messagessupporting

confidence: 67%

Optimally Efficient Multi-party Fair Exchange and Fair Secure Multi-party Computation

Kılınç¹,

KüpçüAlpteki̇n

2021

ACM Trans. Priv. Secur.

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Multi-party fair exchange (MFE) and fair secure multi-party computation (fair SMPC) are under-studied fields of research, with practical importance. In particular, we consider MFE scenarios where at the end of the protocol, either every participant receives every other participant’s item, or no participant receives anything. We analyze the case where a trusted third party (TTP) is optimistically available, although we emphasize that the trust put on the TTP is only regarding the fairness , and our protocols preserve the privacy of the exchanged items against the TTP. In the fair SMPC case, we prove that a malicious TTP can only harm fairness, but not security . We construct an asymptotically optimal multi-party fair exchange protocol that requires a constant number of rounds (in comparison to linear) and O(n 2 ) messages (in comparison to cubic), where n is the number of participating parties. In our protocol, we enable the parties to efficiently exchange any item that can be efficiently put into a verifiable encryption (e.g., signatures on a contract). We show how to apply this protocol on top of any SMPC protocol to achieve fairness with very little overhead (independent of the circuit size). We then generalize our protocol to efficiently handle any exchange topology (participants exchange items with arbitrary other participants). Our protocol guarantees fairness in its strongest sense: even if all n-1 other participants are malicious and colluding with each other, the fairness is still guaranteed.

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Section: Num Messagessupporting

confidence: 67%

Optimally Efficient Multi-party Fair Exchange and Fair Secure Multi-party Computation

Kılınç¹,

KüpçüAlpteki̇n

2021

ACM Trans. Priv. Secur.

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“…Weak fairness implies that the honest parties might have different outputs as long as they can prove their honest behavior. On the contrary, our optimal message efficiency + 2n − 3 applies to any topology, and employs a stronger fairness definition than [37]. Thus their result does not imply our Theorem 1 and vice versa.…”

Section: Optimal Optimistic Schemesmentioning

confidence: 77%

“…Draper-Gil et al [37] determined the minimal message complexity of contract signing schemes with weak fairness on four topologies. Weak fairness implies that the honest parties might have different outputs as long as they can prove their honest behavior.…”

Section: Optimal Optimistic Schemesmentioning

confidence: 99%

Optimal Fair Computation

Guerraoui

Wang

2016

Lecture Notes in Computer Science

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A computation scheme among n parties is fair if no party obtains the computation result unless all other n − 1 parties obtain the same result. A fair computation scheme is optimistic if n honest parties can obtain the computation result without resorting to a trusted third party.We prove, for the first time, a tight lower bound on the message complexity of optimistic fair computation for n parties among which n − 1 can be malicious in an asynchronous network. We do so by relating the optimal message complexity of optimistic fair computation to the length of the shortest permutation sequence in combinatorics.

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“…Some authors [19,[21][22][23][24][25][26][27][28][29][30][31] used definitions that fit the definition we have called hard-timeliness. While other authors [8,9,13,15,[32][33][34][35][36][37][38][39][40][41][42][43][44][45] used definitions that fit the definition we have called soft-timeliness. Some introduced nuance: predetermined time [34,37], agreed time [35,36], bounded time [39], or that the protocol defines a deadline [45].…”

Section: Timelinessmentioning

confidence: 99%

A Hard-Timeliness Blockchain-Based Contract Signing Protocol

Ferrer-Gomila,

Hinarejos

2023

Computers

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In this article, we present the first proposal for contract signing based on blockchain that meets the requirements of fairness, hard-timeliness, and bc-optimism. The proposal, thanks to the use of blockchain, does not require the use of trusted third parties (TTPs), thus avoiding a point of failure and the problem of signatories having to agree on a TTP that is trusted by both. The presented protocol is fair because it is designed such that no honest signatory can be placed at a disadvantage. It meets the hard-timeliness requirement because both signatories can end the execution of the protocol at any time they wish. Finally, the proposal is bc-optimistic because blockchain functions are only executed in case of exception (and not in each execution of the protocol), with consequent savings when working with public blockchains. No previous proposal simultaneously met these three requirements. In addition to the above, this article clarifies the concept of timeliness, which previously has been defined in a confusing way (starting with the authors who used the term for the first time). We conducted a security review that allowed us to verify that our proposal meets the desired requirements. Furthermore, we provide the specifications of a smart contract designed for the Ethereum blockchain family and verified the economic feasibility of the proposal, ensuring it can be aligned with the financial requirements of different scenarios.

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On the Efficiency of Multi-party Contract Signing Protocols

Cited by 5 publications

References 16 publications

Optimally Efficient Multi-party Fair Exchange and Fair Secure Multi-party Computation

Optimally Efficient Multi-party Fair Exchange and Fair Secure Multi-party Computation

Optimal Fair Computation

A Hard-Timeliness Blockchain-Based Contract Signing Protocol

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