Scaling Zero Knowledge UNSAT Verification via Normalized Chaining

📅 2026-09-16
📈 Citations: 0
✨ Influential: 0
📄 PDF
🤖 AI Summary
该研究通过归一化链证明技术改进了ZkUnsat协议,解决了零知识UNSAT验证中的可扩展性和内存消耗问题。
📝 Abstract
Proofs of UNSAT are a standard primitive in formal verification and software assurance. In many real-world settings, the proof itself encodes proprietary or security-sensitive information, making public disclosure undesirable. Zero-knowledge certification of UNSAT addresses this tension: it enables a prover to convince a verifier that no satisfying assignment exists, without revealing anything about the underlying proof beyond its validity. Luo et al. recently introduced ZkUnsat, a protocol that achieves this goal by proving the validity of a weakened resolution proof in zero knowledge. ZkUnsat demonstrates the feasibility of zero-knowledge certification; however, its scalability to larger, real-world instances is constrained by substantial prover memory overhead, limiting its real-world applicability. Motivated by advances in UNSAT proof formats such as LRAT, which enable efficient plain-text verification, we present a preprocessing technique that improves the efficiency of ZkUnsat without introducing additional leakage. Our approach normalizes the proof so that each derived clause is justified by a resolution chain of fixed public length k. This eliminates chain-length leakage and reduces prover memory usage. With k = 16, our method certifies roughly 62% more instances than baseline ZkUnsat on the SAT 2002 competition benchmarks. Furthermore, for an equivalent number of certified instances, the memory footprint drops to under 25% of that required by the baseline.
Problem

Research questions and friction points this paper is trying to address.

Zero-knowledge UNSAT
Scalability
Prover memory overhead
Resolution proof
LRAT
Innovation

Methods, ideas, or system contributions that make the work stand out.

Normalized Chaining
Zero-Knowledge Certification
UNSAT Proof
Memory Efficiency
Proof Normalization
🔎 Similar Papers
No similar papers found.
A
Ashwin Karthikeyan
University of Toronto, Canada
E
Ethan Kharitonov
Independent, Canada
Kuldeep S. Meel
Kuldeep S. Meel
Associate Professor, University of Toronto
Beyond NPAutomated ReasoningFormal MethodsArtificial Intelligence
A
Anwar Hithnawi
University of Toronto, Canada