IEEE/ACM Trans Comput Biol Bioinform. 2020 May-Jun;17(3):930-937. doi: 10.1109/TCBB.2018.2810180. Epub 2018 Feb 27.
The rapid growth of DNA-sequencing technologies motivates more personalized and predictive genetic-oriented services, which further attract individuals to increasingly release their genome information to learn about personalized medicines, disease predispositions, genetic compatibilities, etc. Individual genome information is notoriously privacy-sensitive and highly associated with relatives. In this paper, we present an inference attack algorithm to predict target genotypes and phenotypes based on belief propagation in factor graphs. With this algorithm, an attacker can effectively predict the target genotypes and phenotypes of target individuals based on genome information shared by individuals or their relatives, and genotype and phenotype association from genome-wide association study (GWAS). To address the privacy threats resulted from such inference attacks, we elaborate the metrics to evaluate data utility and privacy and then present a data sanitization method. We evaluate our inference attack algorithm and data sanitization method on real GWAS dataset: Age-related macular degeneration (AMD) case/control dataset. The evaluation results show that our work can effectively defense against genome threats while guaranteeing data utility.
DNA 测序技术的快速发展推动了更个性化和更具预测性的遗传导向服务，这进一步吸引个人越来越多地发布他们的基因组信息，以了解个性化药物、疾病易感性、基因相容性等。个体基因组信息是众所周知的隐私敏感信息，并且与亲属高度相关。在本文中，我们提出了一种基于因子图中的置信传播进行预测目标基因型和表型的推理攻击算法。利用该算法，攻击者可以有效地根据个体或其亲属共享的基因组信息以及全基因组关联研究（GWAS）中的基因型和表型关联，预测目标个体的目标基因型和表型。为了解决这种推理攻击带来的隐私威胁，我们详细阐述了评估数据效用和隐私的指标，然后提出了一种数据净化方法。我们在真实的 GWAS 数据集上评估了我们的推理攻击算法和数据净化方法：年龄相关性黄斑变性（AMD）病例/对照数据集。评估结果表明，我们的工作可以在保证数据效用的同时，有效地防御基因组威胁。
