Private record linkage protocols allow multiple parties to exchange matching records, which refer to the same entities or have similar values, while keeping the non-matching ones secret. Conventional protocols are based on computationally expensive cryptographic primitives and therefore do not scale. To address these scalability issues, hybrid protocols have been proposed that combine differential privacy techniques with secure multiparty computation techniques. However, a drawback of such protocols is that they disclose to the parties both the matching records and the differentially private synopses of the datasets involved in the linkage. Consequently, differential privacy is no longer always satisfied. To address this issue, we propose a novel framework that separates the private synopses from the matching records. The two parties do not access the synopses directly, but still use them to efficiently link records. We theoretically prove the security of our framework under the state-of-the-art privacy notion of differential privacy for record linkage (DPRL). In addition, we develop a simple but effective strategy for releasing private synopses. Extensive experimental results show that our framework is superior to the existing methods in terms of efficiency.

中文翻译：

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混合私人记录链接

私有记录链接协议允许多方交换匹配记录，这些记录引用相同的实体或具有相似的值，同时对不匹配的记录保密。传统协议基于计算量大的密码原语，因此无法扩展。为了解决这些可扩展性问题，已经提出了将差分隐私技术与安全多方计算技术相结合的混合协议。然而，此类协议的一个缺点是它们向各方披露了匹配记录和链接中涉及的数据集的不同私有概要。因此，差分隐私不再总是得到满足。为了解决这个问题，我们提出了一个新颖的框架，将私有概要与匹配记录分开。双方不直接访问概要，但仍然使用它们来有效地链接记录。我们在最先进的记录链接差分隐私（DPRL）隐私概念下从理论上证明了我们框架的安全性。此外，我们制定了一种简单而有效的策略来发布私人概要。大量的实验结果表明，我们的框架在效率方面优于现有方法。