COLLAGENE enables privacy-aware federated and collaborative genomic data analysis

Abstract

Growing regulatory requirements set barriers around genetic data sharing and collaborations. Moreover, existing privacy-aware paradigms are challenging to deploy in collaborative settings. We present COLLAGENE, a tool base for building secure collaborative genomic data analysis methods. COLLAGENE protects data using shared-key homomorphic encryption and combines encryption with multiparty strategies for efficient privacy-aware collaborative method development. COLLAGENE provides ready-to-run tools for encryption/decryption, matrix processing, and network transfers, which can be immediately integrated into existing pipelines. We demonstrate the usage of COLLAGENE by building a practical federated GWAS protocol for binary phenotypes and a secure meta-analysis protocol. COLLAGENE is available at https://zenodo.org/record/8125935.

Genomic privacy preservation in genome-wide association studies: taxonomy, limitations, challenges, and vision

“…Another interesting work is found in [ 46 ], where the authors proposed a federated analytics system, namely FAMHE, based on multiparty homomorphic encryption, enabling privacy preserving of distributed datasets with no linkage of intermediate data. Paper [ 58 ] presented COLLAGENE, a framework for building a secure federated pipeline and collaborative genomic data analysis. COLLAGENE combines homomorphic encryption and secure multi-party computation for federated protection while decreasing time and complexity overhead.…”