论合成基因组数据的实用性和隐私性
On Utility and Privacy in Synthetic Genomic Data
摘要：
基因组数据的可用性对于生物医学研究、个性化医疗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发布或共享基因组数据成为问题，如果不是完全不可能的话。因此，已经启动了几项倡议来试验合成基因组数据，例如，使用生成模型来学习真实数据的底层分布，并生成保留其显着特征而不暴露它的人工数据集。本文首次评估了六种用于生成合成基因组数据的最新模型的实用性和隐私保护。我们评估了合成数据在几个常见任务上的性能，例如等位基因群体统计和连锁不平衡。然后，我们通过成员推断攻击的镜头来衡量隐私，即，推断记录是否是训练数据的一部分。我们的实验表明，没有一种生成合成基因组数据的方法可以同时产生高实用性和强隐私性。此外，训练数据集的大小和性质也很重要。此外，虽然数据集和模型的某些组合产生的合成数据具有接近真实数据的分布，但通常存在易受成员推断影响的目标数据点。展望未来，我们的技术可以被从业者用来评估在野外部署合成基因组数据的风险，并作为未来工作的基准。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
