Design of a Privacy-Preserving Data Platform for Collaboration Against Human Trafficking

TL;DR

This paper introduces a comprehensive privacy-preserving data platform that enables secure sharing and analysis of sensitive human trafficking victim data through anonymization, synthetic data generation, and visual analytics tools.

Contribution

It presents a novel pipeline combining data anonymization, synthetic data creation, and visual interfaces to facilitate secure, useful, and accessible data sharing for anti-human trafficking efforts.

Findings

Generated synthetic data reduces privacy risks.

Aggregate data maintains statistical utility.

Visual analytics improve stakeholder understanding.

Abstract

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.