Privacy issues limit the analysis and cross-exploration of most distributed and private biobanks, often raised by the multiple dimensionality and sensitivity of the data associated with access restrictions and policies. These characteristics prevent collaboration between entities, constituting a barrier to emergent personalized and public health challenges, namely the discovery of new druggable targets, identification of disease-causing genetic variants, or the study of rare diseases. In this paper, we propose a semi-automatic methodology for the analysis of distributed and private biobanks. The strategies involved in the proposed methodology efficiently enable the creation and execution of unified genomic studies using distributed repositories, without compromising the information present in the datasets. We apply the methodology to a case study in the current Covid-19, ensuring the combination of the diagnostics from multiple entities while maintaining privacy through a completely identical procedure. Moreover, we show that the methodology follows a simple, intuitive, and practical scheme.

隐私问题限制了大多数分布式生物银行和私有生物银行的分析和交叉探索，而这往往是由于与访问限制和政策相关的数据的多维性和敏感性而引起的。这些特征阻止了实体之间的协作，从而构成了应对新出现的个性化和公共卫生挑战的障碍，即发现新的可药物化靶标，鉴定致病的基因变异或罕见病的研究。在本文中，我们提出了一种半自动方法，用于分析分布式和私人生物库。所提出的方法中涉及的策略可以有效地使用分布式存储库创建和执行统一的基因组研究，而不会损害数据集中存在的信息。我们将该方法应用于当前Covid-19中的案例研究，以确保将来自多个实体的诊断信息组合在一起，同时通过完全相同的过程来保护隐私。此外，我们表明该方法遵循一个简单，直观和实用的方案。