Mechanism Design Enhances Blockchain Consensus Truthfulness and Scalability
This research introduces novel mechanism design principles to fortify blockchain consensus, ensuring truthful block proposals and mitigating fork-related coordination failures.
Social Paradigm Detection Bolsters Byzantine Fault Tolerance in Blockchains
A novel social-inspired mechanism detects Byzantine nodes with high confidence, enhancing blockchain resilience beyond static fault assumptions.
Game Theory Reveals Incentive-Driven Vulnerabilities in Blockchain Robustness.
This research unifies distributed systems and game theory to expose how rational validator incentives compromise Ethereum Proof-of-Stake safety and liveness, paving the way for resilient protocol design.
Angelfish Unifies Optimal Blockchain Throughput and Latency
Angelfish introduces a hybrid consensus protocol, seamlessly blending leader-based efficiency with DAG-based scalability, promising high throughput and low latency for decentralized systems.
Financial Services Firms Significantly Increase Blockchain Investments
Strategic investments in distributed ledger technology by financial institutions are accelerating, driving operational efficiencies and enabling new market capabilities through proven at-scale applications.
Formalizing Economic Security for Permissionless Consensus Protocols with Slashing
This research formalizes economic security for permissionless consensus, demonstrating how slashing mechanisms in Proof-of-Stake can enhance network resilience.
Angelfish Consensus: Optimizing Throughput and Latency in BFT Protocols
A novel hybrid consensus protocol, Angelfish, dynamically balances leader-based and DAG-based approaches, achieving optimal throughput and latency for Byzantine fault-tolerant systems.
Modular Random Variable Commitments Enable Universal Certified Privacy
This work establishes modularity for random variable commitments, enabling provably private data analysis across arbitrary distributions.
ZKPoT Consensus Secures Federated Learning with Proofs
This research introduces a novel Zero-Knowledge Proof of Training consensus, enabling privacy-preserving federated learning by verifying model contributions without exposing sensitive data.
