Privacy Vulnerabilities in Synthetic Single-Cell RNA-Sequence Data

Motivation Single-cell RNA-Sequence (scRNA-seq) data is kept under strict access control because of its sensitive nature. Synthetic data generation (SDG) techniques can generate artificial scRNA-seq data, begging the question of whether leading SDG techniques like scDesign2 can sufficiently protect privacy of donors to justify relaxing access control.

We present an adversarial privacy attack against scDesign2, demonstrating that the leading technique for synthetic scRNA-seq data generation does not mask which donors were used to train the generator. The fewer the number of cell donors used in the dataset to train the SDG model, the easier an attack is. Using our experimental results, we argue for best practices to safely release synthetic data, mitigating privacy risks. Availability and Implementation https://github.com/steveng9/scRNA-seq_privacy_audits Competing Interest Statement The authors have declared no competing interest.