Lattice-Based SNARKs Achieve Practical Post-Quantum Proof Size Reduction
A new lattice-based zkSNARK construction reduces post-quantum proof size by $10.3times$, collapsing the massive overhead that hindered quantum-secure verifiable computation.
Non-Interactive Quantum Knowledge Arguments Achieve Transparent Setup and Extractable Security
A new non-interactive quantum proof system uses coset state authentication to achieve transparent setup and extractable security, advancing post-quantum verifiable computation.
Characterizing ZKP GPU Bottlenecks Accelerates Verifiable Computation Scaling
ZKProphet empirically identifies Number-Theoretic Transform as the 90% GPU bottleneck, shifting optimization focus to unlock practical ZKP scaling.
Lantern Achieves Short, Post-Quantum Zero-Knowledge Proofs via Polynomial Product Systems
Lantern is a post-quantum ZKP protocol that uses polynomial product proofs to prove vector norms, making proofs 2-3X smaller for scalable, quantum-safe privacy.
Efficient Transparent Zero-Knowledge Proofs Eliminate Trusted Setup for Scalability
A new recursive polynomial commitment scheme, LUMEN, achieves the efficiency of trusted-setup SNARKs while maintaining full transparency, unlocking truly scalable and trustless rollups.
Black-Box Succinct Proofs Achieve Statistical Zero-Knowledge Security
A new polynomial commitment scheme enables succinct zero-knowledge proofs from minimal assumptions, establishing a theoretically optimal foundation for verifiable computation.
Folding Schemes Enable Constant-Time Recursive Zero-Knowledge Proofs
Introducing the folding scheme primitive, Nova bypasses complex SNARK recursion, achieving the fastest prover time and a constant-sized verifier circuit for scalable verifiable computation.
Lattice-Based SNARKs Achieve Post-Quantum Security and Proof Efficiency
Lattice-based proofs, rooted in the SIS problem, enable quantum-resistant, succinct zero-knowledge arguments, securing future computation.
ZKPoT Secures Decentralized Machine Learning by Proving Training without Revealing Data
A new ZKPoT consensus uses zk-SNARKs to cryptographically verify decentralized AI model training performance while preserving data privacy.
