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.
Silently Verifiable Proofs Achieve Constant Communication Batch Zero-Knowledge Verification
Silently Verifiable Proofs introduce a zero-knowledge primitive that enables constant-cost batch verification, unlocking massive private data aggregation and rollup scaling.
Lattice-Based Zero-Knowledge SNARKs Achieve Post-Quantum Security and Transparency
Labrador introduces a lattice-based zkSNARK that future-proofs blockchain privacy and scalability against the quantum computing threat.
Transparent Polynomial Commitment Achieves Succinct Proofs without Trusted Setup
A novel polynomial commitment scheme achieves cryptographic transparency and logarithmic verification, eliminating the reliance on a trusted setup for scalable zero-knowledge proofs.
Hyper-Efficient Prover Unlocks Universal Transparent Zero-Knowledge Scaling
This new HyperPlonk scheme achieves linear prover time for universal transparent SNARKs, fundamentally accelerating verifiable computation for all decentralized applications.
