Reproducibility-Oriented and Privacy-Preserving Genomic Dataset Sharing

The increasing pace in genomic research has brought a high demand for genomic datasets in recent years, yet few studies have released their datasets due to privacy concerns. This poses a problem while validating and reproducing the published results. In this work, in order to promote reproducibility of genome-related research, we propose a novel scheme for sharing genomic datasets under differential privacy, which consists of two stages. In the first step, the scheme generates a noisy copy of the genomic dataset by encoding the data entries as binary values and then XORing them with binary noise, that is calibrated and sampled with optimized time complexity, while considering the biology properties of the datasets. In the second step, the scheme alters the value distribution of each column in the generated copy to align with the privacy-preserving version (protected by the Laplace mechanism) of the distribution in the original dataset using optimal transport. We evaluate the scheme on two realistic genomic datasets from OpenSNP~\cite{opensnp} and compare it with two existing privacy-preserving techniques from NIST challenges~\cite{nist} in regard to GWAS reproducibility (e.g., the $\chi^2$ and the odd ratio test) and other data utility metrics (e.g., point error and mean error). The results show that our scheme outperforms the two methods in GWAS reproducibility by $30\%$ with lower time complexity and achieves higher data utility for other applications as well beyond reproducibility. We also validate via experiments that our scheme achieves high protection against both genomic and machine learning-based inference attacks. The experiment results show that, by constraining the privacy leakage, our mechanism is able to encourage the sharing of a genomic dataset along with the research results on it.