Anonymous Multi-Hop Locks Secure Private Payment Channels Enhancing Blockchain Scalability
Anonymous Multi-Hop Locks (AMHLs) are a new primitive that secures payment channels against fee theft, ensuring both privacy and scalable off-chain transfers.
FairFlow Protocol Enforces Equitable Transaction Ordering Mitigating Extractable Value
This mechanism uses commit-reveal cryptography and incentives to decouple block proposal from transaction ordering, radically reducing MEV and ensuring systemic fairness.
Decentralized Functional Encryption Secures Multi-Party Private Computation without Trust
This new cryptographic primitive enables multiple independent parties to compute joint functions on encrypted data, eliminating the central authority trust bottleneck.
Asynchronous Atomic Broadcast Ensures Optimal Fair Transaction Ordering
The new AOAB protocol uses absolute timestamps in an asynchronous setting to achieve communication-optimal, MEV-resistant transaction finality.
Commitment-Reveal Decouples Ordering from Value to Ensure Fairness
A novel two-phase commitment-reveal protocol decouples transaction ordering from content knowledge, eliminating block producer MEV extraction and ensuring provably fair sequencing.
Commitment-Decay Mechanism Secures Decentralized Private Transaction Ordering Fairness
A Commitment-Decay Mechanism uses economic bonds and parameter commitments to provably secure fair transaction ordering in decentralized private pools.
Cryptographic Proof Systems Decouple Computation and Trustless Verification
Cryptographic proof systems enable trustless outsourcing of complex computation, drastically reducing verification cost for resource-constrained clients.
Transaction Encryption and Ordering Randomization Mitigate Extractable Value
A new mechanism design model integrates transaction encryption and execution randomization to eliminate block producer control, ensuring provably fair transaction ordering and system integrity.
Zero-Knowledge Proof of Training Secures Decentralized Federated Learning Consensus
ZKPoT uses zk-SNARKs to verify decentralized model accuracy without revealing private data, solving the efficiency-privacy trade-off in federated learning.
