Snowveil: New gossip-based framework for decentralized preference discovery

Snowveil, proposed by researcher Grammateia Kotsialou, solves the problem of aggregating subjective preferences without a trusted central authority. The framework operates as a gossip-based protocol: each agent repeatedly samples random peers' rankings and updates its local belief about the winning alternative. Using a potential function, submartingale theory, and concentration bounds, the authors prove that the system converges to the canonical outcome (e.g., the result of applying a given social choice rule to the global profile) with high probability in finite expected time. This single-winner process can be iterated to handle multi-winner scenarios. Snowveil is agnostic to specific aggregation rules—requiring only that the rule satisfies Positive Responsiveness—making it modular and extensible.

The paper also introduces the Constrained Hybrid Borda (CHB) rule, designed to balance broad consensus with plurality support. Extensive simulations validate Snowveil's O(n) scalability, showing that consensus emerges efficiently even in large-scale decentralized systems. This work provides a formal foundation for decentralized preference discovery, with potential applications in DAOs, decentralized governance, and censorship-resistant decision-making. The framework ensures reliability under partial information, asynchrony, and no central coordinator, marking a significant step toward practical, secure social choice in web3 environments.