Design of a Privacy-Preserving Data Platform for Collaboration Against Human Trafficking
This work addresses the critical need for secure data sharing among government, business, and civil society to combat human trafficking, representing a domain-specific application with incremental methodological improvements.
The authors tackled the problem of sharing sensitive human trafficking victim data by developing a privacy-preserving platform that generates synthetic and aggregate datasets with visual analytics interfaces, enabling global collaboration while mitigating privacy and utility risks.
Case records on victims of human trafficking are highly sensitive, yet the ability to share such data is critical to evidence-based practice and policy development across government, business, and civil society. We present new methods to anonymize, publish, and explore such data, implemented as a pipeline generating three artifacts: (1) synthetic data mitigating the privacy risk that published attribute combinations might be linked to known individuals or groups; (2) aggregate data mitigating the utility risk that synthetic data might misrepresent statistics needed for official reporting; and (3) visual analytics interfaces to both datasets mitigating the accessibility risk that privacy mechanisms or analysis tools might not be understandable and usable by all stakeholders. We present our work as a design study motivated by the goal of transforming how the world's largest database of identified victims is made available for global collaboration against human trafficking.