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Facilitating Federated Genomic Data Analysis by Identifying Record Correlations while Ensuring Privacy. (arXiv:2203.05664v1 [cs.CR])
March 14, 2022, 1:20 a.m. | Leonard Dervishi, Xinyue Wang, Wentao Li, Anisa Halimi, Jaideep Vaidya, Xiaoqian Jiang, Erman Ayday
cs.CR updates on arXiv.org arxiv.org
With the reduction of sequencing costs and the pervasiveness of computing
devices, genomic data collection is continually growing. However, data
collection is highly fragmented and the data is still siloed across different
repositories. Analyzing all of this data would be transformative for genomics
research. However, the data is sensitive, and therefore cannot be easily
centralized. Furthermore, there may be correlations in the data, which if not
detected, can impact the analysis. In this paper, we take the first step
towards …
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