MEV’s Economic Limits Challenge Blockchain Scaling
A novel MEV auction mechanism and programmable privacy are proposed to unlock true blockchain scalability, mitigating wasteful on-chain competition.
Enhancing Blockchain Privacy and Scalability with Advanced ZK-SNARK Protocols
This research advances zero-knowledge proofs, offering new cryptographic designs to fundamentally improve privacy and scaling for decentralized systems.
Formalizing MEV: Foundations for Secure Blockchain Mechanism Design
This research formalizes Maximal Extractable Value, providing a rigorous framework for understanding and mitigating systemic blockchain vulnerabilities.
Zkfuzz: Robust Zero-Knowledge Circuit Verification through Fuzzing
zkFuzz formalizes zero-knowledge circuit vulnerabilities and employs novel fuzzing to enhance cryptographic system integrity.
Formalizing Maximal Extractable Value: A Universal Game-Theoretic Framework
This research establishes a universal, game-theoretic definition for Maximal Extractable Value, fundamentally reframing economic attacks within public blockchains for systematic mitigation.
ZKPoT Consensus Secures Federated Learning Privacy and Efficiency
A novel ZKPoT consensus leverages zk-SNARKs to privately validate federated learning contributions, significantly boosting security and efficiency.
Game Theory Models MEV, Mitigates Extraction with Mechanism Design
This research formalizes Maximal Extractable Value dynamics through a multi-stage game, revealing systemic inefficiencies and quantifying mitigation strategies.
MEV Mitigation via Game Theory and Mechanism Design
This research formally models Maximal Extractable Value dynamics, proving its systemic welfare costs, and proposes cryptographic mechanisms to mitigate its adverse effects on decentralized finance.
LLMs Automate Smart Contract Formal Verification Property Generation
A novel system leverages large language models and retrieval-augmented generation to automate smart contract property creation, enhancing security and accessibility.
