First-Price Auction with Equal Sharing Secures Leaderless Blockchain Transaction Fees
A novel first-price auction mechanism for leaderless blockchains ensures fair transaction fee distribution, fostering robust, decentralized block production.
Zero-Knowledge Commitment Enables Private, Verifiable Mechanism Execution without Mediators
A novel framework leverages zero-knowledge proofs to allow mechanism designers to commit to hidden rules, proving incentive properties and outcome correctness without disclosing the mechanism itself, thereby eliminating trusted intermediaries.
Zero-Knowledge Mechanisms Enable Private, Verifiable Commitment
A novel framework leverages zero-knowledge proofs to execute economic mechanisms privately, ensuring verifiable commitment without revealing sensitive design parameters.
Zero-Knowledge Mechanisms Decouple Commitment from Disclosure in Mechanism Design
A novel framework leverages zero-knowledge proofs to enable verifiable, private mechanism execution without trusted mediators, preserving strategic equivalence.
Batch Processing Eliminates MEV in Automated Market Makers
This research introduces a novel batch-processing mechanism for Automated Market Makers, fundamentally mitigating Miner Extractable Value and fostering equitable transaction execution.
Uncertainty Principles Quantify MEV Trade-Offs in Blockchain Transaction Ordering
This research introduces uncertainty principles to model the fundamental trade-off between transaction reordering flexibility and user economic outcomes, revealing limits of universal MEV mitigation.
New Desideratum for Transaction Fee Mechanisms Reveals Inherent Design Trade-Offs
Introducing "off-chain influence proofness" reveals fundamental trade-offs in blockchain transaction fee mechanism design, critical for equitable value distribution.
Incentivizing Federated Edge Learning with Blockchain Mechanism Design
This research introduces a Stackelberg game model and ADMM algorithm to motivate edge servers, enabling optimal resource contribution in decentralized AI training.
New Incentive Mechanism Secures Oracles against Mirroring Attacks
This research introduces a novel reward mechanism to prevent Sybil-like mirroring attacks in decentralized oracles, ensuring data integrity and fair compensation.
