Zero-Knowledge Proof of Training Secures Decentralized Federated AI Consensus
ZKPoT leverages zk-SNARKs to prove AI model quality without revealing private data, solving the privacy-utility trade-off in decentralized learning.
Verifiable Functional Encryption Enables Constant-Cost Decentralized Computation Scaling
A new Verifiable Threshold Functional Encryption primitive achieves constant-size partial decryption, fundamentally solving the linear communication cost bottleneck for large-scale private computation.
Distributed Threshold Cryptography Eliminates Single Point of Failure Key Management
This framework introduces a Distributed Threshold Key Management System, using DKG to shard master keys, fundamentally securing decentralized applications.
Zero-Knowledge Proof Consensus Secures Decentralized Machine Learning without Accuracy Trade-Offs
ZKPoT consensus uses zk-SNARKs to privately verify model training quality, resolving the efficiency-privacy trade-off in decentralized AI.
