MBeacon: Privacy-Preserving Beacons for DNA Methylation Data

Inken Hagestedt, Yang Zhang, Mathias Humbert, Pascal Berrang, Haixu Tang, Xiaofeng Wang, M. Backes
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引用次数: 38

Abstract

The advancement of molecular profiling techniques fuels biomedical research with a deluge of data. To facilitate data sharing, the Global Alliance for Genomics and Health established the Beacon system, a search engine designed to help researchers find datasets of interest. While the current Beacon system only supports genomic data, other types of biomedical data, such as DNA methylation, are also essential for advancing our understanding in the field. In this paper, we propose the first Beacon system for DNA methylation data sharing: MBeacon. As the current genomic Beacon is vulnerable to privacy attacks, such as membership inference, and DNA methylation data is highly sensitive, we take a privacy-by-design approach to construct MBeacon. First, we demonstrate the privacy threat, by proposing a membership inference attack tailored specifically to unprotected methylation Beacons. Our experimental results show that 100 queries are sufficient to achieve a successful attack with AUC (area under the ROC curve) above 0.9. To remedy this situation, we propose a novel differential privacy mechanism, namely SVT2 , which is the core component of MBeacon. Extensive experiments over multiple datasets show that SVT2 can successfully mitigate membership privacy risks without significantly harming utility. We further implement a fully functional prototype of MBeacon which we make available to the research community
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MBeacon: DNA甲基化数据的隐私保护信标
分子分析技术的进步为生物医学研究提供了大量的数据。为了促进数据共享,全球基因组学与健康联盟建立了Beacon系统,这是一个搜索引擎,旨在帮助研究人员找到感兴趣的数据集。虽然目前的Beacon系统只支持基因组数据,但其他类型的生物医学数据,如DNA甲基化,对于促进我们对该领域的理解也至关重要。在本文中,我们提出了第一个用于DNA甲基化数据共享的信标系统:MBeacon。鉴于目前的基因组信标容易受到成员推理等隐私攻击,且DNA甲基化数据高度敏感,我们采用基于隐私的设计方法构建MBeacon。首先,我们通过提出专门针对未受保护的甲基化信标的成员推理攻击来演示隐私威胁。我们的实验结果表明,100个查询足以实现AUC (ROC曲线下的面积)大于0.9的成功攻击。为了解决这种情况,我们提出了一种新的差分隐私机制,即SVT2,它是MBeacon的核心组件。在多个数据集上进行的大量实验表明,SVT2可以在不显著损害效用的情况下成功地降低会员隐私风险。我们进一步实现了MBeacon的全功能原型,并将其提供给研究社区
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