Secure Cross-Silo Synthetic Genomic Data Generation
For institutions needing to share sensitive genomic data for AI development, this work provides a practical solution that combines MPC and DP to enable secure cross-silo synthetic data generation.
This paper tackles the problem of generating synthetic genomic data from multiple sites without revealing raw data, combining secure multiparty computation (MPC) for input privacy and differential privacy (DP) for output privacy. The method produces high-utility synthetic datasets from real RNA-seq cohorts in federated settings, enabling privacy-preserving data synthesis across institutions.
Access to genomic data is highly regulated due to its sensitive nature. While safeguards are essential, cumbersome data access processes pose a significant barrier to the development of AI methods for genomics. Synthetic data generation can mitigate this tension by enabling broader data sharing without exposing sensitive information. Synthetic genomic data are produced by training generative models on real data and subsequently sampling artificial data that preserves relevant statistics while limiting disclosures about the underlying individuals. In some settings, a single data holder may have sufficient data to train such generative models; however, in many applications data must be combined across multiple sites to achieve adequate scale. This need arises, e.g., in rare disease studies, where individual hospitals typically hold data for only a small number of patients. The solution we present in this paper enables multiple data holders to jointly train a synthetic data generator without revealing their raw data. Our approach combines secure multiparty computation (MPC) to ensure input privacy, so that no party ever discloses its data in unencrypted form, with differential privacy (DP) to provide output privacy by mitigating information leakage from the released synthetic data. We empirically demonstrate the effectiveness of the proposed method by generating high-utility synthetic datasets from multiple real RNA-seq cohorts in federated settings, showing that our approach enables privacy-preserving data synthesis even when data are distributed across institutions.