The availability of genomic data is essential to progress in biomedical research, personalized medicine, etc. However, its extreme sensitivity makes it problematic, if not outright impossible, to publish or share it. As a result, several initiatives have been launched to experiment with synthetic genomic data, e.g., using generative models to learn the underlying distribution of the real data and generate artificial datasets that preserve its salient characteristics without exposing it. This paper provides the first evaluation of both utility and privacy protection of six state-of-the-art models for generating synthetic genomic data. We assess the performance of the synthetic data on several common tasks, such as allele population statistics and linkage disequilibrium. We then measure privacy through the lens of membership inference attacks, i.e., inferring whether a record was part of the training data. Our experiments show that no single approach to generate synthetic genomic data yields both high utility and strong privacy across the board. Also, the size and nature of the training dataset matter. Moreover, while some combinations of datasets and models produce synthetic data with distributions close to the real data, there often are target data points that are vulnerable to membership inference. Looking forward, our techniques can be used by practitioners to assess the risks of deploying synthetic genomic data in the wild and serve as a benchmark for future work.
翻译:基因组数据的提供对于生物医学研究、个性化医学等方面的进展至关重要。然而,基因组数据的极端敏感性使得公布或分享合成基因组数据即使不是完全不可能,也难以成问题。因此,我们发起了几项倡议,对合成基因组数据进行实验,例如,利用基因化模型来了解真实数据的基本分布情况,并生成不暴露其显著特征的人工数据集。本文件首次评估了六个最先进的合成基因组数据模型的效用和隐私保护。我们评估了合成数据在若干共同任务上的业绩,例如全方位人口统计和联系不均。我们随后从成员推论攻击的角度衡量隐私,即推断记录是否是培训数据的一部分。我们的实验表明,生成合成基因组数据的任何单一方法都没有产生很高的效用和很强的隐私。此外,培训数据集和模型的一些组合生成的合成数据与分布的合成数据相近,即人口统计和链接不均不均匀称。我们利用合成基因组数据作为未来数据基准的目标,因此,通过将数据用于远期分析,因此,将数据定位为未来数据的目标点。