  As genomic research has become increasingly widespread in recent years, few
studies share datasets due to the sensitivity in privacy of genomic records.
This hinders the reproduction and validation of research outcomes, which are
crucial for catching errors (e.g., miscalculations) during the research
process.To the best of our knowledge, we are the first to propose a method of
sharing genomic datasets in a privacy-preserving manner for GWAS outcome
reproducibility.In this work, we introduce a differential privacy-based scheme
for sharing genomic datasets to enhance the reproducibility of genome-wide
association studies (GWAS) outcomes. The scheme involves two stages. In the
first stage, we generate a noisy copy of the target dataset by applying the XOR
mechanism on the binarized (encoded) dataset, where the binary noise generation
considers biological features. However, the initial step introduces significant
noise, making the dataset less suitable for direct GWAS validation. Thus, in
the second stage, we implement a post-processing technique that adjusts the
Minor Allele Frequency (MAF) values in the noisy dataset to align more closely
with those in a publicly available dataset using optimal transport and decode
it back to genomic space. We evaluated the proposed scheme on three real-life
genomic datasets and compared it with a baseline approach and two
synthesis-based solutions with regard to detecting errors of GWAS outcomes,
data utility, and resistance against membership inference attacks (MIAs). Our
scheme outperforms all the comparing methods in detecting GWAS outcome errors,
achieves better utility and provides higher privacy protection against
membership inference attacks (MIAs). By utilizing our method, genomic
researchers will be inclined to share a differentially private, yet of high
quality version of their datasets.
