论文标题
GWAS的安全保护版本的安全和分布式评估
Secure and Distributed Assessment of Privacy-Preserving Releases of GWAS
论文作者
论文摘要
全基因组关联研究（GWAS）确定了遗传变异与可观察到的特征（例如疾病）之间的相关性。先前的工作为基因组数据持有人的联合会提供了保护隐私的分布算法，该算法跨越了多个机构和立法领域，以安全地计算GWAS结果。但是，这些算法的适用性有限，因为它们仍然需要集中式实例来决定是否可以安全地披露GWAS结果，这违反了隐私法规，例如GDPR。在这项工作中，我们介绍了Gendpr，这是一种分布式的中间件，利用可信赖的执行环境（TEE）安全确定可以安全发布的潜在GWAS统计信息的子集。 GENDPR达到的准确性与集中式解决方案相同，但需要较少的数据传输，因为TEES仅交换中间结果，但没有基因组。此外，可以将GendPR配置为容忍所有但诚实但充满好奇的联合会成员，旨在暴露正确的成员基因组。
Genome-wide association studies (GWAS) identify correlations between the genetic variants and an observable characteristic such as a disease. Previous works presented privacy-preserving distributed algorithms for a federation of genome data holders that spans multiple institutional and legislative domains to securely compute GWAS results. However, these algorithms have limited applicability, since they still require a centralized instance to decide whether GWAS results can be safely disclosed, which is in violation to privacy regulations, such as GDPR. In this work, we introduce GenDPR, a distributed middleware that leverages Trusted Execution Environments (TEEs) to securely determine a subset of the potential GWAS statistics that can be safely released. GenDPR achieves the same accuracy as centralized solutions, but requires transferring significantly less data because TEEs only exchange intermediary results but no genomes. Additionally, GenDPR can be configured to tolerate all-but-one honest-but-curious federation members colluding with the aim to expose genomes of correct members.
