Private genome analysis through homomorphic encryption.
Private genome analysis through homomorphic encryption.
复制标题
DOI：
10.1186/1472-6947-15-s5-s3
复制
发表时间：
2015
影响因子：
3.5
通讯作者：
Lauter K
中科院分区：
文献类型：
--
作者：
Kim M;Lauter K
基因组测序技术的快速发展使研究人员能够访问大型基因组数据集。然而，将数据处理外包到云端会给个人隐私带来很高的风险。本文的目的是给出一个实用的解决方案，这个问题使用同态加密。在我们的方法中，所有的计算都可以在不受信任的云中执行，而不需要解密密钥或与数据所有者的任何交互，这保护了基因组数据的隐私。我们提出了评估算法的安全计算的次要等位基因频率和χ 2统计在全基因组关联研究设置。我们还描述了如何私下计算加密的DNA序列之间的汉明距离和近似编辑距离。最后，我们比较了使用两个实用的同态加密方案的性能细节-Gentry，Halevi和Smart的BGV方案和Bos，劳特，洛夫图斯和Naehrig的YASHE方案。YASHE方案的方法在大约2秒内分析了400人的数据，并从311个点中挑选出与疾病相关的变异。对于另一个任务，使用BGV方案，需要大约65秒才能安全地计算大小为5K的DNA序列的近似编辑距离，并找出它们之间的差异。在同态评估深度电路时（如汉明距离算法或近似编辑距离算法），BGV的性能数字优于YASHE。另一方面，YASHE方案用于低次计算，例如病例对照研究中的次要等位基因频率或χ 2检验统计量，是更有效的。
The rapid development of genome sequencing technology allows researchers to access large genome datasets. However, outsourcing the data processing o the cloud poses high risks for personal privacy. The aim of this paper is to give a practical solution for this problem using homomorphic encryption. In our approach, all the computations can be performed in an untrusted cloud without requiring the decryption key or any interaction with the data owner, which preserves the privacy of genome data. We present evaluation algorithms for secure computation of the minor allele frequencies and χ2 statistic in a genome-wide association studies setting. We also describe how to privately compute the Hamming distance and approximate Edit distance between encrypted DNA sequences. Finally, we compare performance details of using two practical homomorphic encryption schemes - the BGV scheme by Gentry, Halevi and Smart and the YASHE scheme by Bos, Lauter, Loftus and Naehrig. The approach with the YASHE scheme analyzes data from 400 people within about 2 seconds and picks a variant associated with disease from 311 spots. For another task, using the BGV scheme, it took about 65 seconds to securely compute the approximate Edit distance for DNA sequences of size 5K and figure out the differences between them. The performance numbers for BGV are better than YASHE when homomorphically evaluating deep circuits (like the Hamming distance algorithm or approximate Edit distance algorithm). On the other hand, it is more efficient to use the YASHE scheme for a low-degree computation, such as minor allele frequencies or χ2 test statistic in a case-control study.