Secure coin-tossing is typically modeled as an input-less functionality, where parties with no private inputs jointly generate a fair coin. In the dishonest majority setting, however, a strongly fair coin-tossing protocol is impossible. To circumvent this barrier, recent work has adopted the weaker notion of game-theoretic fairness, where adversaries are rational parties with preferences for specific outcomes, seeking to bias the coin in their favor. Yet these preferences may encode secret information, making prior protocols that assume preferences are public, fundamentally incompatible with privacy. We initiate a comprehensive study of privacy-preserving game-theoretically fair coin-tossing, where the preferences of honest parties remain private. We propose a simulation-based security framework and a new ideal functionality that reconciles both preference-privacy and game-theoretic fairness. A key ingredient is a certifying authority that authenticates each party’s preference and publishes only aggregate statistics, preventing misreporting while hiding parties' preferences. The functionality guarantees that every honest party receives an output: either a uniform coin; or, if an adversary deviates, a coin that does not increase the adversarial coalition's expected utility. Within this framework, we construct a protocol realizing our ideal functionality under standard cryptographic assumptions that works for both binary and general m -sided coin-tossing. Our binary scheme matches the corruption threshold of Wu-Asharov-Shi (EUROCRYPT '22); our multi-sided scheme builds on Thyagarajan-Wu-Soni (CRYPTO '24) with a corruption guarantee depending on the coalition's preference counts. Technically, our protocols combine authenticated preferences with an anonymous communication layer that decouples identities from preference-dependent actions, together with a deviation-penalty mechanism that enforces game-theoretic fairness. Our work is the first to reconcile game-theoretic fairness with preference privacy, offering new definitional tools and efficient protocols for rational multi-party computation in dishonest majority settings.
Secure coin-tossing is typically modeled as an input-less functionality, where parties with no private inputs jointly generate a fair coin. In the dishonest majority setting, however, a strongly fair coin-tossing protocol is impossible. To circumvent this barrier, recent work has adopted the weaker notion of game-theoretic fairness, where adversaries are rational parties with preferences for specific outcomes, seeking to bias the coin in their favor. Yet these preferences may encode secret information, making prior protocols that assume preferences are public, fundamentally incompatible with privacy. We initiate a comprehensive study of privacy-preserving game-theoretically fair coin-tossing, where the preferences of honest parties remain private. We propose a simulation-based security framework and a new ideal functionality that reconciles both preference-privacy and game-theoretic fairness. A key ingredient is a certifying authority that authenticates each party’s preference and publishes only aggregate statistics, preventing misreporting while hiding parties' preferences. The functionality guarantees that every honest party receives an output: either a uniform coin; or, if an adversary deviates, a coin that does not increase the adversarial coalition's expected utility. Within this framework, we construct a protocol realizing our ideal functionality under standard cryptographic assumptions that works for both binary and general m -sided coin-tossing. Our binary scheme matches the corruption threshold of Wu-Asharov-Shi (EUROCRYPT '22); our multi-sided scheme builds on Thyagarajan-Wu-Soni (CRYPTO '24) with a corruption guarantee depending on the coalition's preference counts. Technically, our protocols combine authenticated preferences with an anonymous communication layer that decouples identities from preference-dependent actions, together with a deviation-penalty mechanism that enforces game-theoretic fairness. Our work is the first to reconcile game-theoretic fairness with preference privacy, offering new definitional tools and efficient protocols for rational multi-party computation in dishonest majority settings.
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.
Security of distributed cryptographic protocols usually requires privacy (inputs of the honest parties remain hidden), correctness (the adversary cannot improperly affect the outcome), and fairness (if the adversary learns the output, all honest parties do also). Cleve's seminal result (STOC '86) implies that satisfying these properties simultaneously is impossible in the presence of dishonest majorities, and led to several proposals for relaxed notions of fairness. While these works also suggest completeness results (i.e., the ability to design protocols which achieve their fairness notion), their assessment is typically of an all-or-nothing nature. In this work we put forth a new approach for defining relaxed fairness guarantees that allows for a quantitative comparison between protocols with regard to the level of fairness they achieve. The basic idea is to use an appropriate utility function to express the preferences of an adversary who wants to violate fairness. We also show optimal protocols with respect to our notion, in both the two-party and multi-party settings.
Secure two-party computation allows two mutually distrusting parties to compute a joint function over their inputs, guaranteeing properties such as \emph{input privacy} or \emph{correctness}. For many tasks, such as joint computation of statistics, it is important that when one party receives the result of the computation, the other party also receives the result. Unfortunately, this property, which is called \emph{fairness}, is unattainable in the two-party setting for arbitrary functions. So weaker variants have been proposed. One such notion, proposed by Pass \etal (EUROCRYPT 2017) is called \emph{Δ-fairness}. Informally, it guarantees that if a corrupt party receives the output in round r and stops participating in the protocol, then the honest party receives the output by round Δ(r). This notion is achieved by using so-called \emph{secure enclaves}. In many settings, Δ-fairness is not sufficient, because a corrupt party is \emph{guaranteed} to receive its output before the honest party, giving the corrupt party an advantage in further interaction. Worse, as Δ is known to the corrupt party, it can abort the protocol when it is most advantageous. We extend the concept of Δ-fairness by introducing a new fairness notion, which we call \emph{hidden Δ-fairness}, which addresses these problems. First of all, under our new notion, a corrupt party may not benefit from aborting, because it may not, with probability $\nicefrac{1}{2}$, learn the result first. Moreover, Δ and other parameters are sampled according to a given distribution and remain unknown to the participants in the computation. We propose a 2PC protocol that achieves hidden Δ-fairness, also using secure enclaves, and prove its security in the Generalized Universal Composability (GUC) framework.
In his seminal result, Cleve [STOC’86] established that secure distributed computation— guaranteeing fairness—is impossible in the presence of dishonest majorities. A generous number of proposals for relaxed notions of fairness ensued this seminal result, by weakening in various ways the desired security guarantees. While these works also suggest completeness results (i.e., the ability to design protocols which achieve their fairness notion), their assessment is typically of an all-or-nothing nature. That is, when presented with a protocol which is not designed to be fair according to their respective notion, they most likely would render it unfair and make no further statement about it. In this work we put forth a comparative approach to fairness. We present new intuitive notions that when presented with two arbitrary protocols, provide the means to answer the question “Which of the protocols is fairer? ” The basic idea is that we can use an appropriate utility function to express the preferences of an adversary who wants to break fairness. Thus, we can compare protocols with respect to how fair they are, placing them in a partial order according to this relative-fairness relation. After formulating such utility-based fairness notions, we turn to the question of finding optimal protocols—i.e., maximal elements in the above partial order. We investigate—and answer—this question for secure function evaluation, both in the two-party and multi-party settings. To our knowledge, the only other fairness notion providing some sort of comparative state-ment is that of 1/p-security (aka “partial fairness”) by Gordon and Katz [Eurocrypt’10]. We also show in this paper that for a special class of utilities our notion strictly implies 1/p-security. In addition, we fix a shortcoming of the definition which is exposed by our comparison, thus strengthening that result. Key words: Cryptographic protocols, secure multi-party computation, fairness, rational protocol design. 1
Coin flipping is one of the most fundamental tasks in cryptographic protocol design. Informally, a coin flipping protocol should guarantee both (1) Completeness: an honest execution of the protocol by both parties results in a fair coin toss, and (2) Security: a cheating party cannot increase the probability of its desired outcome by any significant amount. Since its introduction by Blum, coin flipping has occupied a central place in the theory of cryptographic protocols. In this paper, we explore what are the implications of the existence of secure coin flipping protocols for complexity theory. As exposited recently by Impagliazzo, surprisingly little is known about this question. Previous work has shown that if we interpret the Security property of coin flipping protocols very strongly, namely that nothing beyond a negligible bias by cheating parties is allowed, then one-way functions must exist. However, for even a slight weakening of this security property (for example that cheating parties cannot bias the outcome by any additive constant ε > 0), the only complexity-theoretic implication that was known was that PSPACE ⊈ BPP. We put forward a new attack to establish our main result, which shows that, informally speaking, the existence of any (weak) coin flipping protocol that prevents a cheating adversary from biasing the output by more than 1/4 - ε implies that NP ⊈ BPP. Furthermore, for constant-round protocols, we show that the existence of any (weak) coin flipping protocol that allows an honest party to maintain any noticeable chance of prevailing against a cheating party implies the existence of (infinitely often) one-way functions.
Secure computation is a fundamental problem in modern cryptography in which multiple parties join to compute a function of their private inputs without revealing anything beyond the output of the function. A series of very strong results in the 1980’s demonstrated that any polynomial-time function can be computed while guaranteeing essentially every desired security property. The only exception is the fairness property, which states that no player should receive their output from the computation unless all players receive their output. While it was shown that fairness can be achieved whenever a majority of players are honest, it was also shown that fairness is impossible to achieve in general when half or more of the players are dishonest. Indeed, it was proven that even boolean XOR cannot be computed fairly by two parties.
The fairness property is both natural and important, and as such it was one of the first questions addressed in modern cryptography (in the context of signature exchange). One contribution of this thesis is to survey the many approaches that have been used to guarantee different notions of partial fairness. We then revisit the topic of fairness within a modern security framework for secure computation. We demonstrate that, despite the strong impossibility result mentioned above, certain interesting functions can be computed fairly, even when half (or more) of the parties are malicious. We also provide a new notion of partial fairness, demonstrate feasibility of achieving this notion for a large class of functions, and show impossibility for certain functions outside this class. We consider fairness in the presence of rational adversaries, and, finally, we study the difficulty of achieving fairness by exploring how much external help is necessary for providing fair secure computation.