In traditional rational multi-party computation, parties are assumed to have utility functions and they act according to the utility assumptions. In economics and political scenarios, parties in multi-party computation may have some other properties such as reputation which plays an important role on boosting mutual cooperation in multiparty computation. In this paper we put forward a new kind of parties satisfying reputation assumption and utility assumptions. Meanwhile we construct a social rational multiparty computation protocol under incomplete information and incomplete network to facilitate the implementation of the protocol within constant rounds.
In traditional rational multi-party computation, parties are assumed to have utility functions and they act according to the utility assumptions. In economics and political scenarios, parties in multi-party computation may have some other properties such as reputation which plays an important role on boosting mutual cooperation in multiparty computation. In this paper we put forward a new kind of parties satisfying reputation assumption and utility assumptions. Meanwhile we construct a social rational multiparty computation protocol under incomplete information and incomplete network to facilitate the implementation of the protocol within constant rounds.
Reputation is commonly used in game theory for mutual cooperation. Assuming that parties value their reputation as well as their payoffs just like people in the real world, this paper considers reputation assumptions when defining the utilities. The new utility consists of two parts: the reputation part and payoff part. The parties are redefined according to the new utility definition. Besides, to represent a more realistic adversary model, a new adversary named semi-rational party is proposed. The semi-rational parties behave like the combination of rational and covert parties. At last, a rational two-party secure computation protocol is constructed. The proof of fairness in the presence of the semi-rational party is stressed in this paper.
With the rapid development of mobile devices and wireless technologies, mobile social networks become increasingly available. People can implement many applications on the basis of mobile social networks. Secure computation, like exchanging information and file sharing, is one of such applications. Fairness in secure computation, which means that either all parties implement the application or none of them does, is deemed as an impossible task in traditional secure computation without mobile social networks. Here we regard the applications in mobile social networks as specific functions and stress on the achievement of fairness on these functions within mobile social networks in the presence of two rational parties. Rational parties value their utilities when they participate in secure computation protocol in mobile social networks. Therefore, we introduce reputation derived from mobile social networks into the utility definition such that rational parties have incentives to implement the applications for a higher utility. To the best of our knowledge, the protocol is the first fair secure computation in mobile social networks. Furthermore, it finishes within constant rounds and allows both parties to know the terminal round.
We study multi-party computation in the model where none of n participating parties are honest: they are either rational, acting in their selfish interest to maximize their utility, or adversarial, acting arbitrarily. In this new model, which we call the mixed-behavior model, we define a class of functions that can be computed in the presence of an adversary using a trusted mediator. We then give a protocol that allows the rational parties to emulate the mediator and jointly compute the function such that (1) assuming that each rational party prefers that it learns the output while others do not, no rational party has an incentive to deviate from the protocol; and (2) the rational parties are protected from a malicious adversary controlling ⌈n/2⌉ − 2 of the participants: the 2 adversary can only either cause all rational participants to abort (so no one learns the function they are trying to compute), or can only learn whatever information is implied by the output of the function.
The field of rational cryptography considers the design of cryptographic protocols in the presence of rational agents seeking to maximize local utility functions. This departs from the standard secure multiparty computation setting, where players are assumed to be either honest or malicious. We detail the construction of both a two-party and a multiparty game theoretic framework for constructing rational cryptographic protocols. Our framework specifies the utility function assumptions necessary to realize the privacy, correctness, and fairness guarantees for protocols. We demonstrate that our framework correctly models cryptographic protocols, such as rational secret sharing, where existing work considers equilibrium concepts that yield unreasonable equilibria. Similarly, we demonstrate that cryptography may be applied to the game theoretic domain, constructing an auction market not realizable in the original formulation. Additionally, we demonstrate that modeling players as rational agents allows us to design a protocol that destabilizes coalitions. Thus, we establish a mutual benefit from combining the two fields, while demonstrating the applicability of our framework to real-world market environments. We also give an application of game theory to adversarial interactions where cryptography is not necessary. Specifically, we consider adversarial machine learning, where the adversary is rational and reacts to the presence of a data miner. We give a general extension to classification algorithms that returns greater expected utility for the data miner than existing classification methods.
Abstract. Rational secret sharing was proposed by Halpern and Teague in [12]. The authors show that, in a setting with rational players, secret sharing and multiparty computation are only possible if the actual secret reconstruction round remains unknown to the players. All the subsequent works use a similar approach with different assumptions. We change the direction by bridging cryptography, game theory, and reputation systems, and propose a “social model ” for repeated rational secret sharing. We provide a novel scheme, named socio-rational secret sharing, in which players are invited to each game based on their reputations in the community. The players run secret sharing protocols while founding and sustaining a public trust network. As a result, new concepts such as a rational foresighted player, social game, and social Nash equilibrium are introduced. To motivate our approach, consider a repeated secret sharing game such as “secure auctions”, where the auctioneers receive sealed-bids from the bidders to compute the auction outcome without revealing the losing bids. If we assume each party has a reputation value, we can then penalize (or reward) the players who are selfish (or unselfish) from game to game. We show that this social reinforcement rationally stimulates the players to be cooperative.