Data Station: Delegated, Trustworthy, and Auditable Computation to Enable Data-Sharing Consortia with a Data Escrow
Data Station: Delegated, Trustworthy, and Auditable Computation to Enable Data-Sharing Consortia with a Data Escrow
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DOI：
10.14778/3551793.3551861
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Siyuan Xia;Zhiru Zhu;Chris Zhu;Jinjin Zhao;K. Chard;Aaron J. Elmore;Ian D. Foster;Michael
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文献类型：
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作者：
Siyuan Xia;Zhiru Zhu;Chris Zhu;Jinjin Zhao;K. Chard;Aaron J. Elmore;Ian D. Foster;Michael
池化和共享数据可以增加和分配数据的价值。但是，由于数据一旦共享就不能撤销，因此出于监管、隐私和法律原因需要控制数据发布的场景默认不共享。由于有选择地控制要发布的数据是困难的，现有的少数数据共享联盟通常是围绕数据共享协议建立的，这些协议是由冗长而乏味的一次性谈判产生的。我们介绍数据站，一个数据托管，旨在使数据共享联盟的形成。数据所有者与托管机构共享数据，因为他们知道，未经他们的同意，这些数据不会被泄露。数据用户将其计算委托给托管。数据托管依靠委托计算来执行查询，而不首先释放数据。Data Station利用硬件飞地在参与者之间生成信任，并利用数据和计算的集中化来生成审计日志。我们在不受信任的中介上运行时，对机器学习和数据共享应用程序进行评估。除了重要的定性优势外，我们还表明，Data Station: i)在机器学习应用程序的准确性和运行时间方面优于联邦学习基线；Ii)比其他安全数据共享框架快几个数量级；iii)在关键路径上引入了较小的开销。
Pooling and sharing data increases and distributes its value. But since data cannot be revoked once shared, scenarios that require controlled release of data for regulatory, privacy, and legal reasons default to not sharing. Because selectively controlling what data to release is difficult, the few data-sharing consortia that exist are often built around data-sharing agreements resulting from long and tedious one-off negotiations. We introduce Data Station, a data escrow designed to enable the formation of data-sharing consortia. Data owners share data with the escrow knowing it will not be released without their consent. Data users delegate their computation to the escrow. The data escrow relies on delegated computation to execute queries without releasing the data first. Data Station leverages hardware enclaves to generate trust among participants, and exploits the centralization of data and computation to generate an audit log. We evaluate Data Station on machine learning and data-sharing applications while running on an untrusted intermediary. In addition to important qualitative advantages, we show that Data Station: i) outperforms federated learning baselines in accuracy and runtime for the machine learning application; ii) is orders of magnitude faster than alternative secure data-sharing frameworks; and iii) introduces small overhead on the critical path.