Game-Theoretically Fair Distributed Coin Tossing With Private Preferences

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.