用于差异私有全基因组关联研究的在线算法

Md Momin Al Aziz, Shahin Kamali, N. Mohammed, Xiaoqian Jiang
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引用次数: 4

摘要

医疗记录的数字化提供了大量的功能性科学数据,可以帮助研究人员了解许多疾病的行为。然而,由于人类基因组数据的收集、传播和分析是高度敏感的,这些数据,特别是基因组数据的隐私含义最近浮出水面。有很多隐私攻击依赖于人类基因组的独特性,揭示了数据集中某个参与者或某个群体的存在。因此,目前的数据共享政策排除了任何公开传播,并在基因组学数据发布之前采取了预防措施,阻碍了及时的科学创新。在本文中,我们研究了一种仅从基因组数据而不是整个数据集发布统计数据的方法,并提出了一种用于全基因组关联研究(GWAS)的广义差异私有机制。我们的方法提供了一种可量化的隐私保证,它在中间输出中添加了噪声,但保证了隐私结果的令人满意的准确性。此外,该方法还提供了多个可调参数,数据所有者可以根据最优隐私要求设置这些参数。这些变量作为均衡器,在GWAS的私密性和实用性之间进行平衡。该方法还结合了在线装箱技术[1],该技术进一步线性地限制了隐私损失,根据打开的箱的数量和传入查询的规模增长。最后,我们使用七个不同的GWAS研究来测试我们的方法的性能,并对我们的方法进行了基准测试。实验结果表明,对于1000个任意在线查询,我们的算法在合理的隐私损失下准确率超过80%,并且在多项研究中超过了最先进的方法(即EigenStrat, LMM, TDT)。
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Online Algorithm for Differentially Private Genome-wide Association Studies
Digitization of healthcare records contributed to a large volume of functional scientific data that can help researchers to understand the behaviour of many diseases. However, the privacy implications of this data, particularly genomics data, have surfaced recently as the collection, dissemination, and analysis of human genomics data is highly sensitive. There have been multiple privacy attacks relying on the uniqueness of the human genome that reveals a participant or a certain group’s presence in a dataset. Therefore, the current data sharing policies have ruled out any public dissemination and adopted precautionary measures prior to genomics data release, which hinders timely scientific innovation. In this article, we investigate an approach that only releases the statistics from genomic data rather than the whole dataset and propose a generalized Differentially Private mechanism for Genome-wide Association Studies (GWAS). Our method provides a quantifiable privacy guarantee that adds noise to the intermediate outputs but ensures satisfactory accuracy of the private results. Furthermore, the proposed method offers multiple adjustable parameters that the data owners can set based on the optimal privacy requirements. These variables are presented as equalizers that balance between the privacy and utility of the GWAS. The method also incorporates Online Bin Packing technique [1], which further bounds the privacy loss linearly, growing according to the number of open bins and scales with the incoming queries. Finally, we implemented and benchmarked our approach using seven different GWAS studies to test the performance of the proposed methods. The experimental results demonstrate that for 1,000 arbitrary online queries, our algorithms are more than 80% accurate with reasonable privacy loss and exceed the state-of-the-art approaches on multiple studies (i.e., EigenStrat, LMM, TDT).
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