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Pooling and sharing data increases and distributes its value. But since datacannot be revoked once shared, scenarios that require controlled release ofdata for regulatory, privacy, and legal reasons default to not sharing. Becauseselectively controlling what data to release is difficult, the few data-sharingconsortia that exist are often built around data-sharing agreements resultingfrom long and tedious one-off negotiations. We introduce Data Station, a dataescrow designed to enable the formation of data-sharing consortia. Data ownersshare data with the escrow knowing it will not be released without theirconsent. Data users delegate their computation to the escrow. The data escrowrelies on delegated computation to execute queries without releasing the datafirst. Data Station leverages hardware enclaves to generate trust amongparticipants, and exploits the centralization of data and computation togenerate an audit log. We evaluate Data Station on machine learning anddata-sharing applications while running on an untrusted intermediary. Inaddition to important qualitative advantages, we show that Data Station: i)outperforms federated learning baselines in accuracy and runtime for themachine learning application; ii) is orders of magnitude faster thanalternative secure data-sharing frameworks; and iii) introduces small overheadon the critical path.

Data Station: Delegated, Trustworthy, and Auditable Computation to Enable Data-Sharing Consortia with a Data Escrow