Zero-Knowledge Mechanisms Enable Private Verifiable Commitment
A cryptographic framework uses zero-knowledge proofs to commit to and execute mechanism rules privately, fundamentally solving the disclosure-commitment trade-off in game theory.
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.
Zero-Knowledge Mechanisms Enable Private, Verifiable Mechanism Design
This research introduces a framework for privately committing to and executing economic mechanisms, leveraging zero-knowledge proofs to ensure verifiability without revealing sensitive rules or data, fostering trustless interactions.
XDC Network Integrates Orochi Zkdatabase for Verifiable, Scalable RWA Tokenization
This integration establishes a universal proving layer, enabling cryptographically verifiable real-world assets and dramatically reducing data integrity costs.
