Verifiable Privacy-Preserving Multidimensional Data Aggregation in Smart Healthcare via Blockchain
Hang Zhou, Liang Zhang, Yuhang Ma · 2025
Multidimensional data aggregation is increasingly important in practical applications. There are some challenges, such as the risk of privacy leaks, accurate assessment of communication costs, effective verification of aggregation results, and defense against malicious node attacks. In this paper, we design a secure and efficient framework for multi-dimensional health data aggregation and sharing. This framework innovatively integrates blockchain technology, fog computing, homomorphic encryption, Pedersen commitment, and the ECDSA signature algorithm. By implementing this solution, we not only protect the privacy of aggregated data but also guarantee data consistency and authentication for data providers. Furthermore, by leveraging the homomorphic properties of Pedersen commitments, we can also check the validity of the final aggregation results. Experimental validation has demonstrated the efficiency and feasibility of our approach, indicating it is a practical solution for multidimensional data aggregation.