MTBA: Multi-Task Batch Auditing for Privacy-Preserving Computation via VOLE-Based Polynomial Proofs

Abstract

Large-scale privacy-preserving computation is essential for cross-domain data collaboration, making correctness auditing and accountability crucial for practical deployment. Existing auditing schemes typically use zero-knowledge proofs (ZKPs) to verify computation correctness and blockchain-based stake or reputation mechanisms to constrain dishonest participants. However, ZKP approaches often verify computation tasks or circuit constraints independently, causing high communication and verification overhead in large-scale task scenarios. Meanwhile, existing blockchain-based accountability mechanisms are weakly coupled with cryptographic audit results and lack sufficient adaptability in dynamic environments. In this paper, we propose MTBA, a batch auditing framework for privacy-preserving computation that leverages VOLE correlation to support polynomial proof generation and integrates blockchain-based reputation feedback. MTBA transforms arithmetic-circuit computations into auditable polynomial tasks by encoding multiplication-gate consistency as polynomial relations, converts these tasks into compact polynomial proofs, and introduces polynomial proof aggregation at multi-task for batch auditing. It further records signed audit outcomes on blockchain to support adaptive reputation feedback and malicious party accountability. Experimental results on circuits with up to $10^{7}$ multiplication gates show that MTBA improves auditing throughput by up to 2.7 times faster than conventional independent auditing schemes. These results indicate that MTBA provides a scalable and accountable auditing mechanism for large-scale privacy-preserving computation.