论文标题
I-GWAS：保护隐私的相互依存的全基因组关联研究
I-GWAS: Privacy-Preserving Interdependent Genome-Wide Association Studies
论文作者
论文摘要
全基因组关联研究（GWASE）鉴定了一组个体中与疾病（例如疾病）统计上有关的基因组变异。不幸的是，粗心的GWAS统计数据可能会引起隐私攻击。几项作品试图将安全处理与GWASE的隐私发布版本调和。但是，我们强调，如果GWASE利用个人和基因组变异的重叠集，这些方法仍然很脆弱。在这种情况下，我们表明，即使依靠最先进的技术来保护发行版，对手也可以重建高达28.6％的参与者的基因组变异，并且释放的统计数据最多可促进92.3％的基因组变异。我们介绍了I-GWAS，这是一个新颖的框架，可以安全地计算并释放多个可能相互依存的GWASE的结果。随着新基因组的可用，I-GWA不断发布隐私和无噪声GWA的结果。
Genome-wide Association Studies (GWASes) identify genomic variations that are statistically associated with a trait, such as a disease, in a group of individuals. Unfortunately, careless sharing of GWAS statistics might give rise to privacy attacks. Several works attempted to reconcile secure processing with privacy-preserving releases of GWASes. However, we highlight that these approaches remain vulnerable if GWASes utilize overlapping sets of individuals and genomic variations. In such conditions, we show that even when relying on state-of-the-art techniques for protecting releases, an adversary could reconstruct the genomic variations of up to 28.6% of participants, and that the released statistics of up to 92.3% of the genomic variations would enable membership inference attacks. We introduce I-GWAS, a novel framework that securely computes and releases the results of multiple possibly interdependent GWASes. I-GWAS continuously releases privacy-preserving and noise-free GWAS results as new genomes become available.
