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

arXiv: https://arxiv.org/abs/2005.05688 · HTML: https://mlsanity.com/paper/2005.05688/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2020.

## Built on by (most cited first, 4 of 4)

- [Achilles' Heels: Vulnerable Record Identification in Synthetic Data Publishing](https://mlsanity.com/paper/2306.10308/) (2023)
- [Synthetic is all you need: removing the auxiliary data assumption for membership inference attacks against synthetic data](https://mlsanity.com/paper/2307.01701/) (2023)
- [Causally Constrained Data Synthesis for Private Data Release](https://mlsanity.com/paper/2105.13144/) (2021)
- [Explaining Categorical Feature Interactions Using Graph Covariance and LLMs](https://mlsanity.com/paper/2501.14932/) (2025)

## Abstract (arXiv)

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.