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} $ ：保留隐私的数据集集成和共享

在增强隐私的技术中，要在隐私，效率和可用性（效用）之间取得合理的平衡是不可避免的挑战。为此，我们提出了一种高度实用的解决方案，用于在一组参与者之间进行隐私保护的集成和数据集共享。我们解决方案的核心是新的交互式协议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他/她的数据集进行随机化，从而可以将所得数据集与其他参与者以隐私保护方式贡献的其他随机化数据集合并。我们的方法不需要参与者之间的密钥共享即可集成不同的数据集。反过来，这导致了用户友好和可扩展的解决方案。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：使用它来构造保留结构的数据集成协议。这对于私有，细粒度的网络流量数据集成特别有用。我们在完善的真实理想模拟范式下陈述协议的安全性，并通过以下方面的原型实现来证明实用性：1）医疗数据集和2）DNS和NetFlow数据集。