Private Matching for Compute
Private Matching for Compute
复制标题
计算专用匹配
DOI：
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复制
发表时间：
2020
期刊：
影响因子：
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通讯作者：
Vlad Vlaskin
中科院分区：
文献类型：
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作者：
Prasad Buddhavarapu;Andrew Knox;Payman Mohassel;Shubho Sengupta;Erik Taubeneck;Vlad Vlaskin
我们重新讨论了用于聚合计算的两方私有集相交问题，我们称之为用于计算的私有匹配。在这个问题中，双方希望根据预先商定的标识对他们的两个数据集的交集执行各种下行计算。我们注意到，以前对这一问题的解决方案具有重要的局限性。例如，对任何一方的数据集中的记录的任何更改或更新都会触发私有匹配组件的重新运行；并且不清楚如何在不透露每个单独批次的匹配率的情况下支持一方的集合以小批的方式流到达。我们提出了两种满足这些要求的私有匹配计算问题的新形式，称为私有ID和流私有秘密共享集交(PS3 I)，并针对这两种形式设计了新的基于DDH的构造。我们的实现表明，当利用这些解决方案固有的并行性时，我们可以在一小时内执行大小为1亿条记录的数据集的匹配。
We revisit the problem of two-party private set intersection for aggregate computation which we refer to as private matching for compute . In this problem, two parties want to perform various downstream computation on the intersection of their two datasets according to a previously agreed-upon identifier. We observe that prior solutions to this problem have important limitations. For example, any change or update to the records in either party’s dataset triggers a rerun of the private matching component; and it is not clear how to support a streaming arrival of one party’s set in small batches without revealing the match rate for each individual batch. We introduce two new formulations of the private matching for compute problem meeting these requirements, called private-ID and streaming private secret shared set intersection (PS 3 I), and design new DDH-based constructions for both. Our implementation shows that when taking advantage of the inherent parallelizability of these solutions, we can execute the matching for datasets of size upto 100 million records within an hour.