Secure Multiparty Computation with Partial Fairness

A protocol for computing a functionality is secure if an adversary in this protocol cannot cause more harm than in an ideal computation where parties give their inputs to a trusted party which returns the output of the functionality to all parties. In particular, in the ideal model such computation is fair -- all parties get the output. Cleve (STOC 1986) proved that, in general, fairness is not possible without an honest majority. To overcome this impossibility, Gordon and Katz (Eurocrypt 2010) suggested a relaxed definition -- 1/p-secure computation -- which guarantees partial fairness. For two parties, they construct 1/p-secure protocols for functionalities for which the size of either their domain or their range is polynomial (in the security parameter). Gordon and Katz ask whether their results can be extended to multiparty protocols. We study 1/p-secure protocols in the multiparty setting for general functionalities. Our main result is constructions of 1/p-secure protocols when the number of parties is constant provided that less than 2/3 of the parties are corrupt. Our protocols require that either (1) the functionality is deterministic and the size of the domain is polynomial (in the security parameter), or (2) the functionality can be randomized and the size of the range is polynomial. If the size of the domain is constant and the functionality is deterministic, then our protocol is efficient even when the number of parties is O(log log n) (where n is the security parameter). On the negative side, we show that when the number of parties is super-constant, 1/p-secure protocols are not possible when the size of the domain is polynomial

Topology-Hiding Computation

Secure Multi-party Computation (MPC) is one of the foundational achievements of modern cryptography, allowing multiple, distrusting, parties to jointly compute a function of their inputs, while revealing nothing but the output of the function. Following the seminal works of Yao and Goldreich, Micali and Wigderson and Ben-Or, Goldwasser and Wigderson, the study of MPC has expanded to consider a wide variety of questions, including variants in the attack model, underlying assumptions, complexity and composability of the resulting protocols. One question that appears to have received very little attention, however, is that of MPC over an underlying communication network whose structure is, in itself, sensitive information. This question, in addition to being of pure theoretical interest, arises naturally in many contexts: designing privacy-preserving social-networks, private peer-to-peer computations, vehicle-to-vehicle networks and the ``internet of things\u27\u27 are some of the examples. In this paper, we initiate the study of ``topology-hiding computation\u27\u27 in the computational setting. We give formal definitions in both simulation-based and indistinguishability-based flavors. We show that, even for fail-stop adversaries, there are some strong impossibility results. Despite this, we show that protocols for topology-hiding computation can be constructed in the semi-honest and fail-stop models, if we somewhat restrict the set of nodes the adversary may corrupt

Fair Computation with Rational Players

We consider the problem of fair multiparty computation, where fairness means (informally) that all parties should learn the correct output. A seminal result of Cleve (STOC 1986) shows that fairness is, in general, impossible to achieve if a majority of the parties is malicious. Here, we treat all parties as rational and seek to understand what can be done. Asharov et al. (Eurocrypt 2011) showed impossibility of rational fair computation in the two-party setting, for a particular function and a particular choice of utilities. We observe, however, that in their setting the parties have no strict incentive to compute the function even in an ideal world where fairness is guaranteed. Revisiting the problem, we show that rational fair computation is possible, for arbitrary functions, as long as the parties have a strict incentive to compute the function in an ideal world where fairness is guaranteed. Our results extend to more general utility functions that do not directly correspond to fairness, as well as to the multi-party setting. Our work thus shows a new setting in which game-theoretic considerations can be used to circumvent a cryptographic impossibility result

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status: publishe