Abstract
With the explosive growth of data, it is increasingly important to integrate data. Privacy-preserving record linkage (PPRL) refers to linking multiple data sources, matching the same entity to be shared by all parties, without disclosing other data. However, most existing PPRL methods rely on an untrusted party to generate matching records, which may lead to privacy leakage and is difficult to ensure the security of linkage. Therefore, an efficient multi-party PPRL method based on Blockchain is proposed. First of all, the data is encoded into Bloom Filters and then split to reduce the amount of information shared during the comparison step of PPRL. Then, homomorphic encryption technology is adopted to further protect data privacy. To improve the efficiency, we construct optimized binary storage trees, which store the records to calculate the similarity, to reduce the number of comparisons between records. In our method, an auditable protocol deployed on the Blockchain is introduced, to detect malicious attacks by untrusted parties. Experimental results show that the proposed method has high linkage quality and efficiency, with strong security of linkage.
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Yao, H., Wei, H., Han, S., Shen, D. (2022). Efficient Multi-party Privacy-Preserving Record Linkage Based on Blockchain. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds) Web Information Systems and Applications. WISA 2022. Lecture Notes in Computer Science, vol 13579. Springer, Cham. https://doi.org/10.1007/978-3-031-20309-1_57
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