The proliferation of sequencing technologies in biomedical research has raised many new privacy concerns. These include concerns over the publication of aggregate data at a genomic scale (e.g. minor allele frequencies, regression coefficients). Methods such as differential privacy can overcome these concerns by providing strong privacy guarantees, but come at the cost of greatly perturbing the results of the analysis of interest. Here we investigate an alternative approach for achieving privacy-preserving aggregate genomic data sharing without the high cost to accuracy of differentially private methods. In particular, we demonstrate how other ideas from the statistical disclosure control literature (in particular, the idea of disclosure risk) can be applied to aggregate data to help ensure privacy. This is achieved by combining minimal amounts of perturbation with Bayesian statistics and Markov Chain Monte Carlo techniques. We test our technique on a GWAS dataset to demonstrate its utility in practice.
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机译：生物医学研究中测序技术的激增引起了许多新的隐私问题。这些包括对以基因组规模发布汇总数据的担忧（例如次要等位基因频率，回归系数）。诸如差异隐私之类的方法可以通过提供强大的隐私保证来克服这些担忧，但代价是极大地干扰了感兴趣的分析结果。在这里，我们研究了另一种方法，该方法可实现保留隐私的总体基因组数据共享，而又不会对差分私有方法的准确性造成高昂的代价。特别是，我们演示了如何将统计披露控制文献中的其他想法（特别是披露风险的想法）应用于汇总数据，以帮助确保隐私。这是通过将最少的摄动量与贝叶斯统计量和Markov Chain Monte Carlo技术相结合来实现的。我们在GWAS数据集上测试了我们的技术，以证明其实用性。
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