Efficient Lattice Polynomial Commitments Secure Post-Quantum ZK Systems
A novel lattice-based polynomial commitment scheme achieves post-quantum security with 8000x smaller proofs, enabling practical, scalable ZK-rollups.
APRO Launches Oracle Network Validating AI Data for Real-World Assets
The APRO oracle introduces an AI-validated data layer, creating a new trust primitive essential for scaling compliant RWA tokenization and prediction markets.
Lattice Cryptography Secures Blockchain Longevity against Quantum Threats
Integrating lattice-based cryptography, Proof-of-Stake, and ZKPs creates a quantum-resistant framework, safeguarding decentralized finance's future.
ShelbyServes Launches Decentralized Cloud for AI Workloads, Cutting Enterprise Compute Costs
The decentralized cloud model offers robust outage resilience and up to a 10x cost reduction, fundamentally optimizing the enterprise AI compute stack.
Major Banks Expand Account Tokenization for Real-Time Payment Security
This systemic tokenization of bank accounts secures the core payment rails, reducing fraud exposure and establishing a compliant, scalable foundation for Open Banking data exchange.
Post-Quantum Lattice Commitments Secure Zero-Knowledge Proofs and Future Blockchain Scalability
Greyhound introduces the first concretely efficient lattice-based polynomial commitment, securing verifiable computation against quantum threats.
Logarithmic-Depth Commitments Enable Truly Stateless Blockchain Verification
A new Logarithmic-Depth Merkle-Trie Commitment scheme achieves constant-time verification, enabling light clients to securely validate state without storing it.
The Clearing House Expands Bank Account Tokenization for US Payments Security
The expanded tokenization service for ACH and RTP networks drastically lowers fraud vectors, securing high-volume payments and open banking data integrity.
Zero-Knowledge Proof of Training Secures Decentralized Machine Learning Integrity
The Zero-Knowledge Proof of Training (ZKPoT) mechanism leverages zk-SNARKs to validate model accuracy without exposing private data, enabling provably secure on-chain AI.
