In privacy-enhancing technology, it has been inevitably challenging to strike a reasonable balance between privacy, efficiency, and usability (utility). To this, we propose a highly practical solution for the privacy-preserving integration and sharing of datasets among a group of participants. At the heart of our solution is a new interactive protocol, PrivateLink. Through PrivateLink, each participant is able to randomize his/her dataset via an independent and untrusted third party, such that the resulting dataset can be merged with other randomized datasets contributed by other participants in a privacy-preserving manner. Our approach does not require key sharing among participants in order to integrate different datasets. This, in turn, leads to a user-friendly and scalable solution. Moreover, the correctness of a randomized dataset returned by the third party can be securely verified by the participant. We further demonstrate PrivateLink's general utilities: using it to construct a structure-preserving data integration protocol. This is particularly useful for private, fine-grained integration of network traffic data. We state the security of our protocols under the well-established real-ideal simulation paradigm and demonstrate practicality by a prototype implementation on: 1) healthcare datasets and 2) DNS and NetFlow datasets.

中文翻译：

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$\mathsf{PrivateLink}$：保护隐私的数据集集成和共享


在隐私增强技术中，在隐私、效率和可用性（效用）之间取得合理的平衡不可避免地具有挑战性。为此，我们提出了一种非常实用的解决方案，用于在一组参与者之间保护隐私的集成和数据集共享。我们解决方案的核心是新的交互协议 PrivateLink。通过 PrivateLink，每个参与者都可以通过独立且不受信任的第三方对其数据集进行随机化，以便生成的数据集可以以保护隐私的方式与其他参与者贡献的其他随机数据集合并。我们的方法不需要参与者之间共享密钥来集成不同的数据集。这反过来又带来了用户友好且可扩展的解决方案。此外，参与者可以安全地验证第三方返回的随机数据集的正确性。我们进一步演示了 PrivateLink 的通用实用程序：使用它来构建保留结构的数据集成协议。这对于网络流量数据的私有、细粒度集成特别有用。我们在完善的真实理想模拟范例下陈述了协议的安全性，并通过原型实现证明了实用性：1) 医疗数据集和 2) DNS 和 NetFlow 数据集。