A Contract Data Sharing Model Based on Consortium Blockchain and Local Differential Privacy

Privacy-preserving and sharing for contract data are crucial for enterprise collaboration. However, current approaches combining blockchain and differential privacy face challenges including high computational costs, low data processing efficiency, and trust issues in decentralized privacy mechanisms. To address this, we propose a federated blockchain model based on multi-dimensional local differential privacy. A Multi-Dimensional Randomized Response (MDRR) mechanism is designed to protect privacy while retaining internal attribute correlations. Secondly, we construct a hybrid computation mechanism that integrates consortium blockchain and differential privacy, enabling on-chain scheduling with off-chain efficient computation, thereby significantly reducing computational overhead. Furthermore, we introduce a Trust-Utility Synergistic Optimization (TUSO) mechanism to enhance reliability by combining trust scores and utility. Experiments show superior accuracy, reduced error, and improved efficiency.