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.

翻译：随着近年来基因组研究的日益普及，由于基因组记录对隐私的敏感性，很少有研究会共享数据集。这阻碍了研究成果的复现与验证，而这对在研究过程中发现错误（例如计算错误）至关重要。据我们所知，我们是首个提出在保护隐私的前提下共享基因组数据集以实现全基因组关联研究（GWAS）结果可复现性的方法。 在本工作中，我们引入了一种基于差分隐私的方案，用于共享基因组数据集以增强全基因组关联研究（GWAS）结果的可复现性。该方案包含两个阶段。第一阶段，我们通过将异或（XOR）机制应用于二值化（编码后的）数据集来生成目标数据集的含噪声副本，其中二值噪声的生成考虑了生物学特征。然而，初始步骤引入的噪声过大，导致数据集难以直接用于GWAS验证。因此，在第二阶段，我们实现了一种后处理技术，利用最优传输方法调整含噪数据集中次要等位基因频率（MAF）值，使其与公开数据集中的对应值更接近，并将其解码回基因组空间。 我们在三个真实基因组数据集上评估了所提方案，并与基线方法及两种基于合成的解决方案进行了对比，涉及检测GWAS结果错误的能力、数据效用以及对成员推断攻击（MIA）的抵抗能力。结果表明，我们的方案在检测GWAS结果错误方面优于所有对比方法，同时实现了更好的数据效用，并对成员推断攻击（MIA）提供了更高的隐私保护。利用我们的方法，基因组研究人员将更倾向于共享其数据集的高质量差分隐私版本。