Privacy Vulnerabilities in Synthetic Single-Cell RNA-Sequence Data

Authors

Golob, S., McKeever, P., Pentyala, S., De Cock, M., Peck, J.

Abstract

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. Results: 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.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 25 Jan 2026.

Stats

1-terrible, 9-excellent. How would you rate this preprint? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.