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Enabling Personal Dataflow Sovereignty via Bolt-on Data Escrow
[Submitted on 2 Aug 2024 (v1), last revised 4 Jul 2026 (this ver · 2024-08-03 · via cs.DB updates on arXiv.org

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Abstract:The digital economy is powered by a continuous and massive exchange of personal data. Individuals provide data to platforms in return for services, from social networking and search to health monitoring, entertainment, and access to LLMs. This exchange has created immense value, but it has also established a fundamental asymmetry of power: individuals possess only coarse-grained control over data access rather than fine-grained control over its purpose of use, creating a gap where data can be repurposed for undisclosed uses, e.g., platforms selling the data to data brokers, which results in a critical loss of personal data sovereignty.
This paper frames this socio-technical challenge as a dataflow management problem. We propose a bolt-on data escrow architecture through delegated computation. In our model, instead of data flowing to platforms, platforms delegate their computation to a trustworthy escrow. This inversion empowers individuals with transparency and control over their dataflows. We present four contributions: (1) a dataflow model that explicitly incorporates computational purpose as a first-class primitive; (2) a minimally invasive programming interface, run(access(), compute()), built on a unified relational interface that virtualizes on-device data sources and a computation offloading component; (3) a concrete implementation of our escrow within the Apple ecosystem, demonstrating its practicality; and (4) both qualitative and quantitative evaluations demonstrating that our solution is expressive enough to implement a wide range of dataflows from real-world applications and introduces minimal runtime overhead. In summary, our work serves as a stepping stone toward achieving personal dataflow sovereignty.

Submission history

From: Zhiru Zhu [view email]
[v1] Fri, 2 Aug 2024 21:26:57 UTC (564 KB)
[v2] Fri, 29 Nov 2024 06:55:23 UTC (574 KB)
[v3] Sat, 10 Jan 2026 07:44:59 UTC (581 KB)
[v4] Sat, 4 Jul 2026 13:35:27 UTC (2,360 KB)

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