Log-Space Commitments Enable Hyper-Efficient Recursive Proofs for Scalable State
A novel Log-Space Verifiable Commitment scheme achieves logarithmic verification complexity for continuous state updates, unlocking truly scalable verifiable systems.
Zero-Knowledge Proof of Training Secures Private Federated Learning Consensus
ZKPoT consensus validates machine learning contributions privately using zk-SNARKs, balancing efficiency, security, and data privacy for decentralized AI.
Zero-Knowledge Proof of Training Secures Decentralized Federated Learning
ZKPoT consensus uses zk-SNARKs to verify machine learning contributions privately, resolving the privacy-verifiability trade-off for decentralized AI.
Proof-of-Useful-Work Embeds Zero-Knowledge Proof Generation into Consensus
A new Proof-of-Useful-Work consensus protocol secures the chain by making general-purpose ZK-SNARK computation the core mining puzzle, democratizing verifiable computation.
Constraint-Reduced Circuits Achieve Orders of Magnitude Faster Zero-Knowledge Proving
New Constraint-Reduced Polynomial Circuits (CRPC) primitives cut ZKP complexity from cubic to linear, unlocking practical verifiable AI and ZK-EVMs.
New Transparent Recursive Commitment Scheme Eliminates Trusted Setup Efficiency Trade-Off
LUMEN introduces a novel recursive polynomial commitment scheme, achieving transparent zk-SNARK efficiency on par with trusted-setup protocols.
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.
Hyper-Efficient Universal SNARKs Decouple Proving Cost from Setup
HyperPlonk introduces a new polynomial commitment scheme, achieving a universal and updatable setup with dramatically faster linear-time proving, enabling mass verifiable computation.
Zero-Knowledge Proof of Training Secures Decentralized AI Consensus
A new Zero-Knowledge Proof of Training (ZKPoT) consensus mechanism leverages zk-SNARKs to cryptographically verify model performance, eliminating Proof-of-Stake centralization and preserving data privacy in decentralized machine learning.
