组合差分隐私与安全计算--以伸缩私有记录链接为例
Composing Differential Privacy and Secure Computation: A case study on scaling private record linkage
摘要：
私有记录链接（PRL）是指根据输入匹配规则从双方不信任的数据库中识别相似的记录对的问题。我们确定了PRL解决方案必须确保的三个关键需求：1）匹配对的完美精确度和高召回率，2）端到端隐私的证明，以及3）在输入记录数量中以次二次方式扩展的通信和计算成本。我们表明，PRL的所有现有的解决方案-包括安全的2方计算（S2 PC），以及它们的变种，使用非私有或差分私有（DP）的阻塞，以确保次二次成本-违反至少一个的三个desiderata。特别是，S2 PC技术保证端到端的隐私，但具有低召回或二次成本。相比之下，对于实现次二次成本的解决方案，没有正式的端到端隐私保证。即使对于组成DP和S2 PC的解决方案也是如此：DP不允许发布关于数据库的任何确切信息，而PRL的S2 PC算法允许发布匹配记录。鉴于这一不足，我们提出了一种新的隐私模型，称为输出约束差分隐私，共享DP的强大的隐私保护，但允许真实发布的输出应用到数据的某个功能。我们将此应用到PRL，并表明，满足此隐私模型的协议允许披露的真实匹配记录，但他们的执行是不敏感的存在或不存在一个单一的非匹配记录。我们发现，以前的工作相结合的DP和S2 PC技术，甚至不能满足这个端到端的隐私模型。因此，我们开发新的协议，可证明实现这个端到端的隐私保证，连同其他两个迫切需要的PRL。
英文摘要：
Private record linkage (PRL) is the problem of identifying pairs of records that are similar as per an input matching rule from databases held by two parties that do not trust one another. We identify three key desiderata that a PRL solution must ensure: 1) perfect precision and high recall of matching pairs, 2) a proof of end-to-end privacy, and 3) communication and computational costs that scale subquadratically in the number of input records. We show that all of the existing solutions for PRL - including secure 2-party computation (S2PC), and their variants that use non-private or differentially private (DP) blocking to ensure subquadratic cost - violate at least one of the three desiderata. In particular, S2PC techniques guarantee end-to-end privacy but have either low recall or quadratic cost. In contrast, no end-to-end privacy guarantee has been formalized for solutions that achieve subquadratic cost. This is true even for solutions that compose DP and S2PC: DP does not permit the release of any exact information about the databases, while S2PC algorithms for PRL allow the release of matching records. In light of this deficiency, we propose a novel privacy model, called output constrained differential privacy, that shares the strong privacy protection of DP, but allows for the truthful release of the output of a certain function applied to the data. We apply this to PRL, and show that protocols satisfying this privacy model permit the disclosure of the true matching records, but their execution is insensitive to the presence or absence of a single non-matching record. We find that prior work that combine DP and S2PC techniques even fail to satisfy this end-to-end privacy model. Hence, we develop novel protocols that provably achieve this end-to-end privacy guarantee, together with the other two desiderata of PRL.
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