# Dr.Aid: Supporting Data-governance Rule Compliance for Decentralized Collaboration in an Automated Way

arXiv: https://arxiv.org/abs/2110.01056 · HTML: https://mlsanity.com/paper/2110.01056/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 94 references that are in the map.
- First posted on arXiv 3 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [How do Data Science Workers Collaborate? Roles, Workflows, and Tools](https://mlsanity.com/paper/2001.06684/) (2020)
- [CamFlow: Managed Data-sharing for Cloud Services](https://mlsanity.com/paper/1506.04391/) (2015)
- [User Perceptions of Smart Home IoT Privacy](https://mlsanity.com/paper/1802.08182/) (2018)
- [Decentralizing Privacy Enforcement for Internet of Things Smart Objects](https://mlsanity.com/paper/1804.02161/) (2018)
- [Towards a computer-interpretable actionable formal model to encode data governance rules](https://mlsanity.com/paper/1911.08439/) (2019)

## Built on by (most cited first, 1 of 1)

- [Perennial Semantic Data Terms of Use for Decentralized Web](https://mlsanity.com/paper/2403.07587/) (2024)

## Abstract (arXiv)

Collaboration across institutional boundaries is widespread and increasing today. It depends on federations sharing data that often have governance rules or external regulations restricting their use. However, the handling of data governance rules (aka. data-use policies) remains manual, time-consuming and error-prone, limiting the rate at which collaborations can form and respond to challenges and opportunities, inhibiting citizen science and reducing data providers' trust in compliance. Using an automated system to facilitate compliance handling reduces substantially the time needed for such non-mission work, thereby accelerating collaboration and improving productivity. We present a framework, Dr.Aid, that helps individuals, organisations and federations comply with data rules, using automation to track which rules are applicable as data is passed between processes and as derived data is generated. It encodes data-governance rules using a formal language and performs reasoning on multi-input-multi-output data-flow graphs in decentralised contexts. We test its power and utility by working with users performing cyclone tracking and earthquake modelling to support mitigation and emergency response. We query standard provenance traces to detach Dr.Aid from details of the tools and systems they are using, as these inevitably vary across members of a federation and through time. We evaluate the model in three aspects by encoding real-life data-use policies from diverse fields, showing its capability for real-world usage and its advantages compared with traditional frameworks. We argue that this approach will lead to more agile, more productive and more trustworthy collaborations and show that the approach can be adopted incrementally. This, in-turn, will allow more appropriate data policies to emerge opening up new forms of collaboration.