基于计数Bloom过滤器的可扩展多数据库隐私保护记录链接
Scalable Multi-Database Privacy-Preserving Record Linkage using Counting Bloom Filters
摘要：
隐私保护记录链接（PPRL）旨在集成来自不同组织的多个不同数据库的敏感信息。PPRL方法在医疗保健、国家安全和商业等实际应用领域中的需求越来越大。以前的办法主要侧重于只连接两个数据库以及使用一个专门的连接单位。将PPRL扩展到更多的数据库（多方PPRL）是一个开放的挑战，因为隐私威胁以及记录链接的计算和通信成本随着数据库的数量显着增加。因此，我们提出了一种新的编码方法的敏感数据的基础上计数布隆过滤器（CBF），以提高隐私的多方PPRL。我们还研究了优化，以减少通信和计算成本的CBF为基础的多方PPRL与不使用一个专用的链接单元。用真实数据集进行的实证评估表明了所提出的方法的可行性，并证明了其可扩展性，链接质量和隐私保护。
英文摘要：
Privacy-preserving record linkage (PPRL) aims at integrating sensitive information from multiple disparate databases of different organizations. PPRL approaches are increasingly required in real-world application areas such as healthcare, national security, and business. Previous approaches have mostly focused on linking only two databases as well as the use of a dedicated linkage unit. Scaling PPRL to more databases (multi-party PPRL) is an open challenge since privacy threats as well as the computation and communication costs for record linkage increase significantly with the number of databases. We thus propose the use of a new encoding method of sensitive data based on Counting Bloom Filters (CBF) to improve privacy for multi-party PPRL. We also investigate optimizations to reduce communication and computation costs for CBF-based multi-party PPRL with and without the use of a dedicated linkage unit. Empirical evaluations conducted with real datasets show the viability of the proposed approaches and demonstrate their scalability, linkage quality, and privacy protection.
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