# Routes for breaching and protecting genetic privacy

arXiv: https://arxiv.org/abs/1310.3197 · HTML: https://mlsanity.com/paper/1310.3197/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2013.

## Built on by (most cited first, 7 of 7)

- [Synthesizing Mixed-type Electronic Health Records using Diffusion Models](https://mlsanity.com/paper/2302.14679/) (2023)
- [Privacy-Preserving Customer Churn Prediction Model in the Context of Telecommunication Industry](https://mlsanity.com/paper/2411.01447/) (2024)
- [Towards more accurate and useful data anonymity vulnerability measures](https://mlsanity.com/paper/2403.06595/) (2024)
- [Privacy-Preserving Fair Synthetic Tabular Data](https://mlsanity.com/paper/2503.02968/) (2025)
- [Quantifying Memorization and Privacy Risks in Genomic Language Models](https://mlsanity.com/paper/2603.08913/) (2026)
- [SNPgen: Phenotype-Supervised Genotype Representation and Synthetic Data Generation via Latent Diffusion](https://mlsanity.com/paper/2603.10873/) (2026)
- [Privacy-preserving federated tensor decomposition of single-cell immune data: recovering multicellular programs across institutions](https://mlsanity.com/paper/2606.24938/) (2026)

## Abstract (arXiv)

We are entering the era of ubiquitous genetic information for research, clinical care, and personal curiosity. Sharing these datasets is vital for rapid progress in understanding the genetic basis of human diseases. However, one growing concern is the ability to protect the genetic privacy of the data originators. Here, we technically map threats to genetic privacy and discuss potential mitigation strategies for privacy-preserving dissemination of genetic data.