# A Privacy-Preserving Record Linkage Method Based on Secret Sharing and Blockchain
## 4 Methods
### Output and Audit Module
#### 4.4.4 Record Keeping and Provenance
Beyond the encoding strategy itself, the robustness of the Hamming distance computation process during actual operation critically affects final linkage quality, particularly false positive/negative (FP/FN) rates. Instances where computation nodes fail, do not successfully upload their shares, or (under a malicious model) submit inconsistent or tampered shares, can all impair Hamming distance accuracy. Partial computation node failure or share loss can lead to inaccurate Hamming distance reconstruction, as aggregating all (\(t\)) partial shares submitted by computation nodes is required. This primarily increases False Negatives (reducing recall), as true matches might be missed due to inability to compute an accurate similarity score. While the system's reputation mechanism penalizes failing nodes, thereby indirectly promoting reliability, unaddressed real-time share loss can still negatively affect judgments for specific linkage pairs. Similarly, if computation nodes upload inconsistent or erroneous shares (due to unintentional error or Byzantine behavior) that are not captured by the sampling audit mechanism of verification nodes (driven by the audit verification smart contract), the final aggregated Hamming distance will deviate from the true value. An incorrectly calculated Hamming distance might cause dissimilar records to appear similar (leading to false positives, reducing precision) or similar records to appear dissimilar (leading to False Negatives, reducing recall). The current design relies on sampling audits and the reputation system to deter and detect such issues, but the non-comprehensive nature of sampling audits means some erroneous shares might still affect calculations. In summary, while this method's cryptographic design aims for accurate Hamming distance computation (making linkage quality primarily dependent on the encoding strategy), practical operational factors like computation node reliability, honesty, and network stability can, if not fully mitigated by existing fault tolerance and error correction mechanisms (e.g., reputation management, sampling audits), indirectly affect Hamming distance accuracy. This, in turn, can adversely impact FP/FN rates. Therefore, further enhancing the fault tolerance of the Hamming distance computation process against such node failures and data inconsistencies is an important aspect for improving overall linkage quality stability. ## 6 Experimental Results

### Experiment Preparation

In this section, we evaluate the proposed SSB-PPRL method's performance in three aspects--scalability, linkage quality, and security--through experiments, comparing it with three related methods by [PERSON] et al. (2010); [PERSON] et al. (2015); [PERSON] et al. (2016). [PERSON] et al. 's MP-PPRL-CBT uses a consortium blockchain instead of a semi-trusted third party, integrating homomorphic encryption and MapReduce for secure and efficient multi-party linkage. [PERSON] et al. 's method employs segmented Bloom filters and distributed secure computation for efficient multi-party approximate matching. [PERSON] et al. 's approach introduces twin neural networks to PPRL for adaptive matching via feature learning and proposes a composite Bloom filter to enhance encoding security. The experimental environment consisted of several high-performance servers, each equipped with an AMD EPYC 7763 CPU (64 cores/128 threads, \(\sim\)2.45 GHz base frequency),128 GB DDR4 RAM, high-speed NVMe storage, and Ubuntu 20.04 ITS. The core SSB-PPRL algorithm was implemented in Python 3.8, with cryptographic operations utilizing the PyCryptodome library. Blockchain functionalities were supported by a private Ethereum test network based on Ganache CLI v7.7.0, with smart contracts written in Solidity v0.8.17 and managed by Truffle Suite. For IPFS storage, multiple Kubo v0.18.1 nodes were deployed. These nodes, along with the SSB-PPRL application modules, were containerized using Docker and run in a local high-speed network environment.