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BLOOM: BLoom filter based oblivious outsourced matchings

机译:布卢姆:基于BLoom过滤器的遗忘外包匹配

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Background Whole genome sequencing has become fast, accurate, and cheap, paving the way towards the large-scale collection and processing of human genome data. Unfortunately, this dawning genome era does not only promise tremendous advances in biomedical research but also causes unprecedented privacy risks for the many. Handling storage and processing of large genome datasets through cloud services greatly aggravates these concerns. Current research efforts thus investigate the use of strong cryptographic methods and protocols to implement privacy-preserving genomic computations. Methods We propose Fhe-Bloom and Phe-Bloom , two efficient approaches for genetic disease testing using homomorphically encrypted Bloom filters. Both approaches allow the data owner to securely outsource storage and computation to an untrusted cloud. Fhe-Bloom is fully secure in the semi-honest model while Phe-Bloom slightly relaxes security guarantees in a trade-off for highly improved performance. Results We implement and evaluate both approaches on a large dataset of up to 50 patient genomes each with up to 1000000 variations (single nucleotide polymorphisms). For both implementations, overheads scale linearly in the number of patients and variations, while Phe-Bloom is faster by at least three orders of magnitude. For example, testing disease susceptibility of 50 patients with 100000 variations requires only a total of 308.31 s ( σ =8.73 s) with our first approach and a mere 0.07 s ( σ =0.00 s) with the second. We additionally discuss security guarantees of both approaches and their limitations as well as possible extensions towards more complex query types, e.g., fuzzy or range queries. Conclusions Both approaches handle practical problem sizes efficiently and are easily parallelized to scale with the elastic resources available in the cloud. The fully homomorphic scheme, Fhe-Bloom , realizes a comprehensive outsourcing to the cloud, while the partially homomorphic scheme, Phe-Bloom , trades a slight relaxation of security guarantees against performance improvements by at least three orders of magnitude.
机译:背景技术全基因组测序已变得快速,准确和便宜,为大规模收集和处理人类基因组数据铺平了道路。不幸的是,这个崭新的基因组时代不仅有望在生物医学研究中取得巨大进步,而且还会给许多人带来前所未有的隐私风险。通过云服务处理大型基因组数据集的存储和处理极大地加剧了这些担忧。因此,当前的研究工作调查了使用强密码方法和协议来实现保护隐私的基因组计算。方法我们提出了Fhe-Bloom和Phe-Bloom,这两种使用同态加密Bloom过滤器进行遗传疾病测试的有效方法。两种方法都允许数据所有者将存储和计算安全地外包给不受信任的云。 Fhe-Bloom在半诚实模型中是完全安全的,而Phe-Bloom在权衡取舍时略微放宽了安全性保证,以提高性能。结果我们在多达50个患者基因组的大型数据集上实施和评估了这两种方法,每个基因组具有1000000个变异(单核苷酸多态性)。对于这两种实现方式,间接费用都随患者和变化的数量线性增加,而Phe-Bloom的速度至少快三个数量级。例如,使用我们的第一种方法测试50名100000个变异的患者的疾病易感性仅需要308.31 s(σ= 8.73 s),而使用第二种方法仅需0.07 s(σ= 0.00 s)。我们还将讨论这两种方法及其局限性的安全性保证,以及对更复杂查询类型(例如模糊查询或范围查询)的可能扩展。结论两种方法都可以有效地处理实际问题的大小,并且可以轻松地与云中可用的弹性资源并行扩展。完全同构的方案Fhe-Bloom实现了对云的全面外包,而部分同构的方案Phe-Bloom则对安全保证进行了些许放宽,以防止性能至少提高三个数量级。

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