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A new way to protect privacy in large-scale genome-wide association studies

机译:在大规模的全基因组关联研究中保护隐私的新方法

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Motivation: Increased availability of various genotyping techniques has initiated a race for finding genetic markers that can be used in diagnostics and personalized medicine. Although many genetic risk factors are known, key causes of common diseases with complex heritage patterns are still unknown. Identification of such complex traits requires a targeted study over a large collection of data. Ideally, such studies bring together data from many biobanks. However, data aggregation on such a large scale raises many privacy issues. Results: We show how to conduct such studies without violating privacy of individual donors and without leaking the data to third parties. The presented solution has provable security guarantees.
机译:动机:各种基因分型技术可用性的提高引发了寻找可用于诊断和个性化医学的遗传标记的竞赛。尽管已知许多遗传风险因素,但具有复杂遗传模式的常见疾病的关键原因仍然未知。识别此类复杂特征需要针对大量数据进行有针对性的研究。理想情况下,此类研究应将许多生物库的数据汇总在一起。但是,如此大规模的数据聚合会引发许多隐私问题。结果:我们展示了如何进行此类研究,而又不侵犯单个捐助者的隐私,也不会将数据泄露给第三方。提出的解决方案具有可证明的安全保证。

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