# A Privacy-Preserving Record Linkage Method Based on Secret Sharing and Blockchain
## 1 Introduction  
In the era of big data, cross-institutional data sharing and integration have become key drivers in advancing fields such as medical research, financial analysis, and government decision-making. Record linkage (RL), as a core technology, aims to identify and match records representing the same entity across multiple data sources, providing critical support for data integration and value extraction [1, 2]. However, with the exponential growth of data volumes and increasingly stringent privacy regulations, traditional methods face significant challenges when handling sensitive data. Direct sharing of raw data risks exposing personal information, limiting its widespread use. To address this, privacy-preserving record linkage (PPRL) has emerged, focusing on achieving efficient and accurate record matching without disclosing raw data [3]. For instance, in public health, PPRL can securely link records from different hospitals, transportation systems, or communitiesfor infectious disease contact tracing [4]. While ensuring privacy, it effectively identifies transmission chains and informs containment strategies, complying with relevant data protection regulations. Researchers have proposed various methods for PPRL, most of which are built on the \"honest but curious\" model [3; 5; 6]. This model assumes that participants strictly follow the protocol but may attempt to infer additional information from received data. Such methods typically rely on a semi-trusted third party (STTP) for similarity computation or direct collaborative computation among participants. The former approach risks data privacy leakage due to STTP's potential untrustworthiness, while the latter incurs significant communication overhead and struggles to prevent malicious collusion among participants, posing threats to data security. Some researchers suggest introducing blockchain technology to record and verify the actions of all participants and third parties in the PPRL process, enhancing overall transparency and traceability [7]. While this strategy improves data security to some extent, it still cannot fully prevent collusion among malicious participants [8]. Therefore, cryptographic techniques such as secure multi-party computation and homomorphic encryption are still needed to achieve higher levels of privacy protection [9]. However, these encryption methods often involve significant communication and computational overhead, presenting a trade-off between efficiency and security in practical deployment. In our prior work, we employed a method based on homomorphic encryption to enhance security, but its high computational cost also confirmed this trade-off between efficiency and security [10]. To specifically address this performance bottleneck, we propose a novel PPRL architecture that deeply integrates blockchain with the distributed computation model of secret sharing, offering a more efficient alternative to computationally intensive homomorphic encryption schemes. In this architecture, we first encode data using Bloom filters, then leverage secret sharing to securely decompose computational tasks across multiple distributed nodes. The blockchain acts as a decentralized coordinator and auditor, managing task allocation, data flow (via IPFS), and result verification through smart contracts, while masking and random permutation mechanisms enhance privacy. This architecture allows most computations to be performed locally on the nodes, thereby minimizing inter-node communication and significantly improving overall efficiency. The main contributions are as follows:  
1. We apply secret sharing techniques to data encoded with Bloom filters, introducing masking and random permutation to decompose similarity computations into basic operations that each computation node can independently perform locally.