Zero-Knowledge Proofs Secure Mechanism Design without Revealing Rules
A new cryptographic framework enables verifiable, private mechanism design by using zero-knowledge proofs to commit to rules without public disclosure, eliminating trusted mediators.
Transparent Succinct Proofs Eliminate Trusted Setup and Large Proof Size
A novel Vector Hash Commitment achieves constant-size, transparent proofs, resolving the critical trade-off between ZK-SNARK succinctness and ZK-STARK setup-free security.
Scalable ZK Proofs for Hashing Integrity Unlock Trustless Blockchain Verification
A new ZK proof methodology for cryptographic hashing, leveraging Plonky2, ensures computational integrity for scalable, trustworthy systems.
Sublinear Zero-Knowledge Proofs Democratize Verifiable Computation on Constrained Devices
A novel proof system reduces ZKP memory from linear to square-root scaling, fundamentally unlocking privacy-preserving computation for all mobile and edge devices.
Zero-Knowledge Proof of Training Secures Decentralized AI Consensus Privacy
The ZKPoT mechanism leverages zk-SNARKs to cryptographically verify model training contribution, solving the privacy-centralization dilemma in decentralized AI.
Sublinear Zero-Knowledge Proofs Unlock Ubiquitous Private Computation
A new proof system eliminates ZKP memory bottlenecks by achieving square-root scaling, enabling verifiable computation on all devices.
Lattice-Based Arguments Achieve Succinct Post-Quantum Verification Using Homomorphic Commitments
This work delivers the first lattice-based argument with polylogarithmic verification time, resolving the trade-off between post-quantum security and SNARK succinctness.
Zero-Knowledge Proofs Enable Verifiable, Hidden Economic Mechanisms without Trusted Mediators
Cryptographic commitments hide mechanism rules while zero-knowledge proofs verify incentive compatibility, unlocking private, trustless economic design.
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.
