Jordan Freitas and Eric Brewer

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2021-18

May 1, 2021

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-18.pdf

Open data requirements and concern for privacy in data-driven international development projects are increasingly prevalent. Current practices typically attempt to balance the two by manually removing personally identifying information and publishing a view of the remaining data. Both practically and theoretically this approach fails to satisfy the open data objective of reusability, and fails to protect privacy of individuals in the data. This thesis explores how to improve both the utility of shared data and how well privacy is maintained with strategically designed tools and methods. We propose and evaluate these tools and strategies for collaborative data management to help navigate tensions between open data and data privacy in the context of international development engineering projects. We first share the results of interviews with individuals who work closely with data in one subfield of development engineering and analyze the results in terms of implications for building data management and data sharing tools. From there, we propose design requirements for workflow sharing tools based on four motivating use cases in different areas of development engineering and present our implementation of a tool to satisfy these requirements. We then provide an overview of privacy considerations and our improvement mechanisms. Both our workflow sharing tool and privacy strategies enable more fine-grained control over data and code sharing with an emphasis on usability. Finally, we situate this work politically and socially in the context of international development.

Advisors: Eric Brewer

---

BibTeX citation:

@phdthesis{Freitas:EECS-2021-18,
    Author= {Freitas, Jordan and Brewer, Eric},
    Title= {Collaborative Tools and Strategies for Data-driven Development Engineering},
    School= {EECS Department, University of California, Berkeley},
    Year= {2021},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-18.html},
    Number= {UCB/EECS-2021-18},
    Abstract= {Open data requirements and concern for privacy in data-driven international development projects are increasingly prevalent. Current practices typically attempt to balance the two by manually removing personally identifying information and publishing a view of the remaining data. Both practically and theoretically this approach fails to satisfy the open data objective of reusability, and fails to protect privacy of individuals in the data. This thesis explores how to improve both the utility of shared data and how well privacy is maintained with strategically designed tools and methods. We propose and evaluate these tools and strategies for collaborative data management to help navigate tensions between open data and data privacy in the context of international development engineering projects. We first share the results of interviews with individuals who work closely with data in one subfield of development engineering and analyze the results in terms of implications for building data management and data sharing tools. From there, we propose design requirements for workflow sharing tools based on four motivating use cases in different areas of development engineering and present our implementation of a tool to satisfy these requirements. We then provide an overview of privacy considerations and our improvement mechanisms. Both our workflow sharing tool and privacy strategies enable more fine-grained control over data and code sharing with an emphasis on usability. Finally, we situate this work politically and socially in the context of international development.},
}

EndNote citation:

%0 Thesis
%A Freitas, Jordan 
%A Brewer, Eric 
%T Collaborative Tools and Strategies for Data-driven Development Engineering
%I EECS Department, University of California, Berkeley
%D 2021
%8 May 1
%@ UCB/EECS-2021-18
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-18.html
%F Freitas:EECS-2021-18