Post-Quantum Cryptography Secures Blockchain Foundations against Future Quantum Threats
Integrating post-quantum algorithms into blockchain protocols provides a foundational security layer, preempting the existential threat from quantum computation.
Zero-Knowledge Proof of Training Secures Private Decentralized Machine Learning
ZKPoT consensus uses zk-SNARKs to prove model accuracy privately, resolving the privacy-utility-efficiency trilemma for federated learning.
Accountable Byzantine Consensus Achieves Optimal Communication and Accountability Complexity
New Accountable Byzantine Consensus protocol, `abcopt`, delivers optimal communication complexity while guaranteeing provable validator accountability.
ZKPoT Consensus Secures Federated Learning with Verifiable, Private Model Contributions
Zero-Knowledge Proof of Training (ZKPoT) is a new consensus primitive that cryptographically verifies model accuracy without exposing private training data, resolving the privacy-utility conflict in decentralized AI.
Czech Central Bank Pilots $1 Million Digital Asset Reserve Strategy
The sovereign pilot integrates tokenized deposits and digital assets to establish a compliant, end-to-end operational framework for reserve management and systemic risk mitigation.
VanEck Selects SOL Strategies for Regulated Solana ETF Staking Yield
Integrating institutional-grade staking infrastructure secures compliant yield for a regulated ETF, optimizing capital efficiency and product differentiation.
Post-Quantum Ring Signatures with Acorn Verification Unlock Scalable Private Transactions
Acorn Verification provides post-quantum ring signatures, replacing Fiat-Shamir for fast, private, and secure blockchain transaction authentication.
Lattice-Based Signatures Secure Blockchain against Quantum Attack Overhead Challenge
Lattice-based cryptography replaces vulnerable ECDSA, securing digital signatures against quantum computers while managing significant data overhead.
ZKPoT: Private Consensus Verifies Decentralized Machine Learning
ZKPoT consensus leverages zk-SNARKs to cryptographically verify machine learning model contributions without revealing private training data or parameters.
