Prrr: Personal Random Rewards for Blockchain Reporting
Hongyin Chen, Yubin Ke, Xiaotie Deng, Ittay Eyal
TL;DR
The paper addresses the blockchain reporting problem where timely external data is essential for smart contracts but symmetric reward designs create a security-efficiency trade-off. It proposes Ex-Ante Synthetic Asymmetry (EASA) and the Personal Random Rewards for Reporting (Prrr) mechanism, which assigns random heterogeneous values to reports and uses a second-price-style settlement to achieve incentive compatibility. A formal model of publishers and validators, a three-phase reporting protocol, and a rigorous game-theoretic analysis establish that following Prrr constitutes a Subgame-Perfect Nash Equilibrium robust to collusion and Sybil attacks. The work proves the impossibility of achieving desired properties under symmetric design and demonstrates that Prrr decentralizes reporting while maintaining efficiency, fairness, and progress, with broad applicability to on-chain data publishing across smart contracts.
Abstract
Smart contracts, the stateful programs running on blockchains, often rely on reports. Publishers are paid to publish these reports on the blockchain. Designing protocols that incentivize timely reporting is the prevalent reporting problem. But existing solutions face a security-performance trade-off: Relying on a small set of trusted publishers introduces centralization risks, while allowing open publication results in an excessive number of reports on the blockchain. We identify the root cause of this trade-off to be the standard symmetric reward design, which treats all reports equally. We prove that no symmetric-reward mechanism can overcome the trade-off. We present Personal Random Rewards for Reporting (Prrr), a protocol that assigns random heterogeneous values to reports. We call this novel mechanism-design concept Ex-Ante Synthetic Asymmetry. To the best of our knowledge, Prrr is the first game-theoretic mechanism (in any context) that deliberately forms participant asymmetry. Prrr employs a second-price-style settlement to allocate rewards, ensuring incentive compatibility and achieving both security and efficiency. Following the protocol constitutes a Subgame-Perfect Nash Equilibrium, robust against collusion and Sybil attacks. Prrr is applicable to numerous smart contracts that rely on timely reports.