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.
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.
Verifiable Decapsulation Secures Post-Quantum Key Exchange Implementation Correctness
This new cryptographic primitive enables provable correctness for post-quantum key exchange mechanisms, transforming un-auditable local operations into publicly verifiable proofs of secure shared secret derivation.
ZKPoT Secures Federated Learning Consensus and Model Privacy
The Zero-Knowledge Proof of Training (ZKPoT) mechanism leverages zk-SNARKs to validate model contributions without revealing data, resolving the privacy-efficiency conflict in decentralized AI.
Fast Zero-Knowledge Proofs for Verifiable Machine Learning via Circuit Optimization
The Constraint-Reduced Polynomial Circuit (CRPC) dramatically lowers ZKP overhead for matrix operations, making private, verifiable AI practical.
Zero-Knowledge Proof of Training Secures Federated Learning Consensus
ZKPoT uses zk-SNARKs to verify model contributions privately, eliminating the trade-off between decentralized AI privacy and consensus efficiency.
Zero-Knowledge Proof of Training Secures Private Federated Consensus
A novel Zero-Knowledge Proof of Training (ZKPoT) mechanism leverages zk-SNARKs to validate machine learning contributions privately, enabling a scalable, decentralized AI framework.
ZK Stack Atlas Upgrade Delivers 15,000 TPS and One-Second Finality for AppChains
The Atlas upgrade transforms the ZK Stack into a high-throughput, sub-second finality platform, strategically positioning sovereign ZK-chains for institutional finance.
Optimizing ZK-SNARKs by Minimizing Expensive Cryptographic Group Elements
Polymath redesigns zk-SNARKs by shifting proof composition from $mathbb{G}_2$ to $mathbb{G}_1$ elements, significantly reducing practical proof size and on-chain cost.
