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 challenge in terms of reproducing and validating published research findings, which is necessary to avoid errors (e.g., miscalculations) during the research process.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 stage, the scheme generates a noisy copy of the genomic dataset by conducting the XOR operation between the binarized (encoded) dataset and binary noises. To preserve the biological features, entries of the noises are generated by considering the inherent correlation properties of the genomic data (obtained from publicly available datasets). In the second stage, 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 proposed scheme on two real-life genomic datasets from OpenSNP compared with two existing privacy-preserving techniques, both of which are winners from NIST challenges. In regard to reproducing findings of the genome-wide association studies (considering the $\chi^2$ tests and the odd ratio tests), our scheme can detect even slight errors (e.g., miscalculations) that may occur during the research process, while other methods cannot even identify significant errors. Additionally, we indicate via experiments that our scheme has better data utility and achieves higher protection against membership inference attacks with lower time complexity.

翻译：近年来，基因组研究的加速发展对基因组数据集的需求日益增长，然而由于隐私问题，很少有研究公开其数据集。这给验证和复现已发表的研究成果带来了挑战——而这一过程对于避免研究过程中出现的错误（如计算失误）至关重要。为促进基因组相关研究的可复现性，本文提出了一种基于差分隐私的基因组数据集共享新方案，该方案包含两个阶段。第一阶段：通过对二值化（编码）数据集与二进制噪声进行异或运算，生成原始数据集的含噪副本。为保留生物特征，噪声项基于公开数据集中基因组数据的固有相关性生成。第二阶段：利用最优传输方法，调整生成副本中各列的值分布，使其与原始数据集的隐私保护版本（经拉普拉斯机制保护后的分布）对齐。我们在OpenSNP的两个真实基因组数据集上评估了该方案，并与两种现存隐私保护技术（均为美国国家标准与技术研究院挑战赛优胜方案）进行了对比。在复现全基因组关联分析结果（基于χ²检验与比值比检验）方面，本方案能够检测研究过程中可能出现的细微错误（如计算失误），而其他方法甚至无法识别显著错误。此外，实验表明本方案具有更优的数据效用，能以更低的时间复杂度实现对成员推理攻击的更强防护。