Data protection for the common good: Developing a framework for a data protection-focused data commons

DATA & POLICY(2022)

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摘要
In our data-driven society, personal data affecting individuals as data subjects are increasingly being collected and processed by sizeable and international companies. While data protection laws and privacy technologies attempt to limit the impact of data breaches and privacy scandals, they rely on individuals having a detailed understanding of the available recourse, resulting in the responsibilization of data protection. Existing data stewardship frameworks incorporate data-protection-by-design principles but may not include data subjects in the data protection process itself, relying on supplementary legal doctrines to better enforce data protection regulations. To better protect individual autonomy over personal data, this paper proposes a data protection-focused data commons to encourage co-creation of data protection solutions and rebalance power between data subjects and data controllers. We conduct interviews with commons experts to identify the institutional barriers to creating a commons and challenges of incorporating data protection principles into a commons, encouraging participatory innovation in data governance. We find that working with stakeholders of different backgrounds can support a commons' implementation by openly recognizing data protection limitations in laws, technologies, and policies when applied independently. We propose requirements for deploying a data protection-focused data commons by applying our findings and data protection principles such as purpose limitation and exercising data subject rights to the Institutional Analysis and Development (IAD) framework. Finally, we map the IAD framework into a commons checklist for policy-makers to accommodate co-creation and participation for all stakeholders, balancing the data protection of data subjects with opportunities for seeking value from personal data.
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关键词
commons, data commons, data protection, interviews, personal data
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