# A Privacy-Preserving Record Linkage Method Based on Secret Sharing and Blockchain
## 2 Related Works  
In recent years, research on PPRL methods has primarily focused on privacy protection while also considering linkage quality and computational efficiency [3, 5, 6, 8]. Among these, Bloom filters have been widely adopted in PPRL due to their excellent space efficiency and fast query capabilities, enabling the encoding of identifiers for similarity matching [11]. However, standard Bloom filters are vulnerable to frequency attacks and cryptanalysis, offering relatively limited privacy protection [6]. To enhance security, researchers have proposed various improved encoding techniques, such as adjacent bit encoding [12], to bolster resistance against attacks. While these encoding methods are generally efficient, they may still leak sensitive information in certain scenarios. In contrast, methods based on secure multi-party computation provide stronger privacy guarantees, allowing matching computations without revealing raw data. Homomorphic encryption enables computations directly on encrypted data, mitigating the risk of plaintext exposure [9]. [PERSON] et al. demonstrated a large-scale PPRL instance using secret sharing on the Sharemind platform [13]. Despite the significant privacy advantages of MPC methods, their high computational and communication overheads pose efficiency bottlenecks for large-scale data applications. To enhance system trustworthiness and auditability, particularly in multi-party collaborative environments lacking trust, blockchain technology has also been applied to PPRL. [PERSON] et al. proposed a blockchain-based PPRL method that leverages smart contracts as a STTP to manage computational tasks during linkage [7]. However, this approach was later criticized for potentially exposing sensitive information during the exchange of Bloom filter segments [8]. To address these security concerns, our prior work [10] explored a different direction, integrating homomorphic encryption with a consortium blockchain framework to enable secure, parallel computation. While this approach successfully enhanced security against malicious adversaries, it also highlighted a critical trade-off: the significant computational overhead inherent to homomorphic encryption remained a primary obstacle to scalability and practical deployment. This challenge underscores a gap in the literature for a solution that can retain the trust and auditability benefits of blockchain while utilizing a more lightweight cryptographic primitive to achieve higher efficiency. To improve computational efficiency, researchers have explored various optimization techniques, including partitioning, distributed computing, and parallel computing [14]. Partitioning techniques, such as random blocking based on Locality-Sensitive Hashing, can effectively identify similar record pairs while ensuring theoretical correctness, significantly reducing computational load during matching [15, 16]. Distributed computing frameworks, such as Apache Flink, support large-scale data processing, enhancing the scalability of PPRL when handling massive datasets [16]. Furthermore, parallel computing methods, by integrating technologies like MapReduce, enable efficient data matching and comparison [10], further improving computational efficiency and reducing processing time. This allows PPRL to operate more effectively in large-scale, multi-party environments. In terms of linkage quality, researchers have optimized the accuracy and reliability of record linkage from multiple perspectives. For instance, [PERSON] et al. proposed a novel Bloom filter method for databases with missing values [17]. This approach uses a missing pattern lattice structure to group records and assigns different weights to non-missing attributes to optimize similarity calculations, thereby improving matching accuracy. Additionally, [PERSON] developed a multi-layer active learning protocol that integrates human review mechanisms to dynamically optimize the matching process [18].