Zero-Knowledge Proof of Training Secures Private Decentralized AI Consensus
A new ZKPoT consensus leverages zk-SNARKs to verify model training integrity without revealing private data, solving the privacy-efficiency dilemma.
Zero-Knowledge Proof of Training Secures Federated Consensus
Research introduces ZKPoT consensus, leveraging zk-SNARKs to cryptographically verify machine learning contributions without exposing private training data or model parameters.
Ethereum Foundation Advances End-to-End Protocol Privacy Roadmap
This initiative establishes a comprehensive architectural framework for pervasive privacy, fortifying Ethereum's foundational integrity for global digital interaction.
Ethereum Foundation Advances Privacy with Comprehensive Roadmap Integration
This architectural pivot integrates robust privacy mechanisms across Ethereum's core protocol, enabling confidential transactions and preserving user data integrity.
